Look up lab in Wiktionary, the free dictionary.

Lab or LAB may refer to:

• an informal name for a laboratory, a facility to conduct scientific research.
• a short form for the Labrador Retriever, a breed of dogs
• LaB, an acronym for the company Lily and Becs
• L.A.B. diploma which stands for Licentiate of the Associated Board
• the IATA code for Lablab Airport, an airport in Lablab, Papua New Guinea
• Láb, a village near Bratislava in western Slovakia
• LAB (band), a Finnish band
• Lab color space
• LAB – Linhas Aéreas Brasileiras, a defunct Brazilian airline
• LAB Records, a British-based independent record label
• Lab (river), a river in the north-eastern part of Kosovo
• Latin American Briton, an ethnic group of the United Kingdom
• Lactic acid bacteria, a group of bacteria useful to the dairy, wine, and food industries
• Langile Abertzaleen Batzordeak, a basque national trade union
• League of American Bicyclists, the association of cyclists of the United States, formerly the League of American Wheelmen.
• Library Association of Bangladesh
• Linear alkyl benzene, a common inexpensive surfactant used in many industrial and household detergent formulations
• Linux as bootloader
• Live aus Berlin, a recording of a 1999 concert by the German band Rammstein
• Lloyd Aéreo Boliviano, a defunct Bolivian airline
• Lloyd Your
• Long Aston Bypass
• Los Angeles Baptist High School, a Christian, college preparatory high school

See also[link]

• The Lab (disambiguation)
• Labs (disambiguation)

This disambiguation page lists articles associated with the same title. If an internal link led you here, you may wish to change the link to point directly to the intended article.

In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings (polysemy). The solution to this problem impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, inference et cetera.

Research has progressed steadily to the point where WSD systems achieve sufficiently high levels of accuracy on a variety of word types and ambiguities. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.

Current accuracy is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (homograph) level is routinely above 90%, with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in recent evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.

About[link]

A disambiguation process requires two strict things: a dictionary to specify the senses which are to be disambiguated and a corpus of language data to be disambiguated (in some methods, a training corpus of language examples is also required). WSD task has two variants: "lexical sample" and "all words" task. The former comprises disambiguating the occurrences of a small sample of target words which were previously selected, while in the latter all the words in a piece of running text need to be disambiguated. The latter is deemed a more realistic form of evaluation, but the corpus is more expensive to produce because human annotators have to read the definitions for each word in the sequence every time they need to make a tagging judgement, rather than once for a block of instances for the same target word.

To give a hint how all this works, consider two examples of the distinct senses that exist for the (written) word "bass":

1. a type of fish
2. tones of low frequency

and the sentences:

1. I went fishing for some sea bass.
2. The bass line of the song is too weak.

To a human, it is obvious that the first sentence is using the word "bass (fish)", as in the former sense above and in the second sentence, the word "bass (instrument)" is being used as in the latter sense below. Developing algorithms to replicate this human ability can often be a difficult task, as is further exemplified by the implicit equivocation between "bass (sound)" and "bass" (musical instrument).

History[link]

WSD was first formulated as a distinct computational task during the early days of machine translation in the 1940s, making it one of the oldest problems in computational linguistics. Warren Weaver, in his famous 1949 memorandum on translation,^[1] first introduced the problem in a computational context. Early researchers understood the significance and difficulty of WSD well. In fact, Bar-Hillel (1960) used the above example to argue^[2] that WSD could not be solved by "electronic computer" because of the need in general to model all world knowledge.

In the 1970s, WSD was a subtask of semantic interpretation systems developed within the field of artificial intelligence, but since WSD systems were largely rule-based and hand-coded they were prone to a knowledge acquisition bottleneck.

By the 1980s large-scale lexical resources, such as the Oxford Advanced Learner's Dictionary of Current English (OALD), became available: hand-coding was replaced with knowledge automatically extracted from these resources, but disambiguation was still knowledge-based or dictionary-based.

In the 1990s, the statistical revolution swept through computational linguistics, and WSD became a paradigm problem on which to apply supervised machine learning techniques.

The 2000s saw supervised techniques reach a plateau in accuracy, and so attention has shifted to coarser-grained senses, domain adaptation, semi-supervised and unsupervised corpus-based systems, combinations of different methods, and the return of knowledge-based systems via graph-based methods. Still, supervised systems continue to perform best.

Difficulties[link]

Differences between dictionaries[link]

One problem with word sense disambiguation is deciding what the senses are. In cases like the word bass above, at least some senses are obviously different. In other cases, however, the different senses can be closely related (one meaning being a metaphorical or metonymic extension of another), and in such cases division of words into senses becomes much more difficult. Different dictionaries and thesauruses will provide different divisions of words into senses. One solution some researchers have used is to choose a particular dictionary, and just use its set of senses. Generally, however, research results using broad distinctions in senses have been much better than those using narrow ones.^[3][4] However, given the lack of a full-fledged coarse-grained sense inventory, most researchers continue to work on fine-grained WSD.

Most research in the field of WSD is performed by using WordNet as a reference sense inventory for English. WordNet is a computational lexicon that encodes concepts as synonym sets (e.g. the concept of car is encoded as { car, auto, automobile, machine, motorcar }). Other resources used for disambiguation purposes include Roget's Thesaurus^[5] and Wikipedia.^[6]

Part-of-speech tagging[link]

In any real test, part-of-speech tagging and sense tagging are very closely related with each potentially making constraints to the other. And the question whether these tasks should be kept together or decoupled is still not unanimously resolved, but recently scientists incline to test these things separately (e.g. in the Senseval/SemEval competitions parts of speech are provided as input for the text to disambiguate).

It is instructive to compare the word sense disambiguation problem with the problem of part-of-speech tagging. Both involve disambiguating or tagging with words, be it with senses or parts of speech. However, algorithms used for one do not tend to work well for the other, mainly because the part of speech of a word is primarily determined by the immediately adjacent one to three words, whereas the sense of a word may be determined by words further away. The success rate for part-of-speech tagging algorithms is at present much higher than that for WSD, state-of-the art being around 95% accuracy or better, as compared to less than 75% accuracy in word sense disambiguation with supervised learning. These figures are typical for English, and may be very different from those for other languages.

Inter-judge variance[link]

Another problem is inter-judge variance. WSD systems are normally tested by having their results on a task compared against those of a human. However, while it is relatively easy to assign parts of speech to text, training people to tag senses is far more difficult.^[7] While users can memorize all of the possible parts of speech a word can take, it is often impossible for individuals to memorize all of the senses a word can take. Moreover, humans do not agree on the task at hand – give a list of senses and sentences, and humans will not always agree on which word belongs in which sense.^[8]

Thus, a computer cannot be expected to give better performance on such a task than a human (indeed, since the human serves as the standard, the computer being better than the human is incoherent),^{[citation needed]} so the human performance serves as an upper bound. Human performance, however, is much better on coarse-grained than fine-grained distinctions, so this again is why research on coarse-grained distinctions^[9][10] has been put to test in recent WSD evaluation exercises.^[3][4]

Common sense[link]

Some AI researchers like Douglas Lenat argue that one cannot parse meanings from words without some form of common sense ontology. For example, comparing these two sentences:

• "Jill and Mary are sisters." – (they are sisters of each other).
• "Jill and Mary are mothers." – (each is independently a mother).

To properly identify senses of words one must know common sense facts.^[11] Moreover, sometimes the common sense is needed to disambiguate such words like pronouns in case of having anaphoras or cataphoras in the text.

Sense inventory and algorithms' task-dependency[link]

A task-independent sense inventory is not a coherent concept^[12]: each task requires its own division of word meaning into senses relevant to the task. For example, the ambiguity of 'mouse' (animal or device) is not relevant in English-French machine translation, but is relevant in information retrieval. The opposite is true of 'river', which requires a choice in French (fleuve 'flows into the sea', or rivière 'flows into a river').

Also, completely different algorithms might be required by different applications. In machine translation, the problem takes the form of target word selection. Here, the "senses" are words in the target language, which often correspond to significant meaning distinctions in the source language ("bank" could translate to the French "banque" -- that is, 'financial bank' or "rive" -- that is, 'edge of river'). In information retrieval, a sense inventory is not necessarily required, because it is enough to know that a word is used in the same sense in the query and a retrieved document; what sense that is, is unimportant.

Discreteness of senses[link]

Finally, the very notion of "word sense" is slippery and controversial. Most people can agree in distinctions at the coarse-grained homograph level (e.g., pen as writing instrument or enclosure), but go down one level to fine-grained polysemy, and disagreements arise. For example, in Senseval-2, which used fine-grained sense distinctions, human annotators agreed in only 85% of word occurrences.^[13] Word meaning is in principle infinitely variable and context sensitive. It does not divide up easily into distinct or discrete sub-meanings.^[14] Lexicographers frequently discover in corpora loose and overlapping word meanings, and standard or conventional meanings extended, modulated, and exploited in a bewildering variety of ways. The art of lexicography is to generalize from the corpus to definitions that evoke and explain the full range of meaning of a word, making it seem like words are well-behaved semantically. However, it is not at all clear if these same meaning distinctions are applicable in computational applications, as the decisions of lexicographers are usually driven by other considerations. Recently, a task – named lexical substitution – has been proposed as a possible solution to the sense discreteness problem.^[15] The task consists of providing a substitute for a word in context that preserves the meaning of the original word (potentially, substitutes can be chosen from the full lexicon of the target language, thus overcoming discreteness).

Approaches and methods[link]

As in all natural language processing, there are two main approaches to WSD – deep approaches and shallow approaches.

Deep approaches presume access to a comprehensive body of world knowledge. Knowledge, such as "you can go fishing for a type of fish, but not for low frequency sounds" and "songs have low frequency sounds as parts, but not types of fish", is then used to determine in which sense the word is used. These approaches are not very successful in practice, mainly because such a body of knowledge does not exist in a computer-readable format, outside of very limited domains.^[16] However, if such knowledge did exist, then deep approaches would be much more accurate than the shallow approaches.^{[citation needed]} Also, there is a long tradition in computational linguistics, of trying such approaches in terms of coded knowledge and in some cases, it is hard to say clearly whether the knowledge involved is linguistic or world knowledge. The first attempt was that by Margaret Masterman and her colleagues, at the Cambridge Language Research Unit in England, in the 1950s. This attempt used as data a punched-card version of Roget's Thesaurus and its numbered "heads", as an indicator of topics and looked for repetitions in text, using a set intersection algorithm. It was not very successful,^[17] but had strong relationships to later work, especially Yarowsky's machine learning optimisation of a thesaurus method in the 1990s.

Shallow approaches don't try to understand the text. They just consider the surrounding words, using information such as "if bass has words sea or fishing nearby, it probably is in the fish sense; if bass has the words music or song nearby, it is probably in the music sense." These rules can be automatically derived by the computer, using a training corpus of words tagged with their word senses. This approach, while theoretically not as powerful as deep approaches, gives superior results in practice, due to the computer's limited world knowledge. However, it can be confused by sentences like The dogs bark at the tree which contains the word bark near both tree and dogs.

There are four conventional approaches to WSD:

• Dictionary- and knowledge-based methods: These rely primarily on dictionaries, thesauri, and lexical knowledge bases, without using any corpus evidence.
• Supervised methods: These make use of sense-annotated corpora to train from.
• Semi-supervised or minimally supervised methods: These make use of a secondary source of knowledge such as a small annotated corpus as seed data in a bootstrapping process, or a word-aligned bilingual corpus.
• Unsupervised methods: These eschew (almost) completely external information and work directly from raw unannotated corpora. These methods are also known under the name of word sense discrimination.

Almost all these approaches normally work by defining a window of n content words around each word to be disambiguated in the corpus, and statistically analyzing those n surrounding words. Two shallow approaches used to train and then disambiguate are Naïve Bayes classifiers and decision trees. In recent research, kernel-based methods such as support vector machines have shown superior performance in supervised learning. Graph-based approaches have also gained much attention from the research community, and currently achieve performance close to the state of the art.

Dictionary- and knowledge-based methods[link]

The Lesk algorithm^[18] is the seminal dictionary-based method. It is based on the hypothesis that words used together in text are related to each other and that the relation can be observed in the definitions of the words and their senses. Two (or more) words are disambiguated by finding the pair of dictionary senses with the greatest word overlap in their dictionary definitions. For example, when disambiguating the words in "pine cone", the definitions of the appropriate senses both include the words evergreen and tree (at least in one dictionary).

An alternative to the use of the definitions is to consider general word-sense relatedness and to compute the semantic similarity of each pair of word senses based on a given lexical knowledge base such as WordNet. Graph-based methods reminiscent of spreading activation research of the early days of AI research have been applied with some success. More complex graph-based approaches have been shown to perform almost as well as supervised methods^[19] or even outperforming them on specific domains.^[3][20] Recently, it has been reported that simple graph connectivity measures, such as degree, perform state-of-the-art WSD in the presence of a sufficiently rich lexical knowledge base.^[21] Also, automatically transferring knowledge in the form of semantic relations from Wikipedia to WordNet has been shown to boost simple knowledge-based methods, enabling them to rival the best supervised systems and even outperform them in a domain-specific setting.^[22]

The use of selectional preferences (or selectional restrictions) is also useful, for example, knowing that one typically cooks food, one can disambiguate the word bass in "I am cooking basses" (i.e., it's not a musical instrument).

Supervised methods[link]

Supervised methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words (hence, world knowledge and reasoning are deemed unnecessary). Probably every machine learning algorithm going has been applied to WSD, including associated techniques such as feature selection, parameter optimization, and ensemble learning. Support Vector Machines and memory-based learning have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature space. However, these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense-tagged corpora for training, which are laborious and expensive to create.

Semi-supervised methods[link]

Because of the lack of training data, many word sense disambiguation algorithms use semi-supervised learning, which allows both labeled and unlabeled data. The Yarowsky algorithm was an early example of such an algorithm.^[23] It uses the ‘One sense per collocation’ and the ‘One sense per discourse’ properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a given collocation.

The bootstrapping approach starts from a small amount of seed data for each word: either manually tagged training examples or a small number of surefire decision rules (e.g., 'play' in the context of 'bass' almost always indicates the musical instrument). The seeds are used to train an initial classifier, using any supervised method. This classifier is then used on the untagged portion of the corpus to extract a larger training set, in which only the most confident classifications are included. The process repeats, each new classifier being trained on a successively larger training corpus, until the whole corpus is consumed, or until a given maximum number of iterations is reached.

Other semi-supervised techniques use large quantities of untagged corpora to provide co-occurrence information that supplements the tagged corpora. These techniques have the potential to help in the adaptation of supervised models to different domains.

Also, an ambiguous word in one language is often translated into different words in a second language depending on the sense of the word. Word-aligned bilingual corpora have been used to infer cross-lingual sense distinctions, a kind of semi-supervised system.

Unsupervised methods[link]

Unsupervised learning is the greatest challenge for WSD researchers. The underlying assumption is that similar senses occur in similar contexts, and thus senses can be induced from text by clustering word occurrences using some measure of similarity of context,^[24] a task referred to as word sense induction or discrimination. Then, new occurrences of the word can be classified into the closest induced clusters/senses. Performance has been lower than other methods, above, but comparisons are difficult since senses induced must be mapped to a known dictionary of word senses. If a mapping to a set of dictionary senses is not desired, cluster-based evaluations (including measures of entropy and purity) can be performed. Alternatively, word sense induction methods can be tested and compared within an application. For instance, it has been shown that word sense induction improves Web search result clustering by increasing the quality of result clusters and the degree diversification of result lists.^[25] It is hoped that unsupervised learning will overcome the knowledge acquisition bottleneck because they are not dependent on manual effort.

Other approaches[link]

Other approaches may vary differently in their methods:

• Identification of dominant word senses;^[26][27][28]
• Domain-driven disambiguation;^[29][30]
• WSD using Cross-Lingual Evidence.^[31][32]

Local impediments and summary[link]

The knowledge acquisition bottleneck is perhaps the major impediment to solving the WSD problem. Unsupervised methods rely on knowledge about word senses, which is barely formulated in dictionaries and lexical databases. Supervised methods depend crucially on the existence of manually annotated examples for every word sense, a requisite that can so far be met only for a handful of words for testing purposes, as it is done in the Senseval exercises.

Therefore, one of the most promising trends in WSD research is using the largest corpus ever accessible, the World Wide Web, to acquire lexical information automatically.^[33] WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as information retrieval (IR). In this case, however, the reverse is also true: Web search engines implement simple and robust IR techniques that can be successfully used when mining the Web for information to be employed in WSD. Therefore, the lack of training data provoked appearing some new algorithms and techniques described here:

External knowledge sources[link]

Knowledge is a fundamental component of WSD. Knowledge sources provide data which are essential to associate senses with words. They can vary from corpora of texts, either unlabeled or annotated with word senses, to machine-readable dictionaries, thesauri, glossaries, ontologies, etc. They can be^[34][35] classified as follows:

1. Structured:
   • Thesauri
   • Machine-readable dictionaries (MRDs)
   • Ontologies
2. Unstructured:
   • Corpora: raw corpora and sense-annotated corpora
   • Collocation resources
   • Other resources (such as word frequency lists, stoplists, domain labels,^[36] etc.)

Evaluation[link]

Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. Before the organization of specific evaluation campaigns most systems were assessed on in-house, often small-scale, data sets. In order to test one's algorithm, developers should spend their time to annotate all word occurrences. And comparing methods even on the same corpus is not eligible if there is different sense inventories.

In order to define common evaluation datasets and procedures, public evaluation campaigns have been organized. Senseval (now renamed SemEval) is an international word sense disambiguation competition, held every three years since 1998: Senseval-1 (1998), Senseval-2 (2001), Senseval-3 (2004), and its successor, SemEval (2007). The objective of the competition is to organize different lectures, preparing and hand-annotating corpus for testing systems, perform a comparative evaluation of WSD systems in several kinds of tasks, including all-words and lexical sample WSD for different languages, and, more recently, new tasks such as semantic role labeling, gloss WSD, lexical substitution, etc. The systems submitted for evaluation to these competitions usually integrate different techniques and often combine supervised and knowledge-based methods (especially for avoiding bad performance in lack of training examples).

Task Design Choices[link]

Sense Inventories. During the first Senseval workshop the HECTOR sense inventory was adopted. The reason for adopting a previously unknown sense inventory was mainly to avoid the use of popular fine-grained word senses (such as WordNet), which could make the experiments unfair or biased. However, given the lack of coverage of such inventories, since the second Senseval workshop the WordNet sense inventory has been adopted.

A set of testing words. Comparison of methods can be divided in 2 groups by amount of words to test. The difference consists in the amount of analysis and processing:

• all-words task implies disambiguating all the words of the text
• lexical sample consists in disambiguating some previously chosen target words.

It is assumed that the former one is more realistic evaluation, although with very laborious testing of results. Initially only the latter was used in evaluation but later the former was included.

Lexical sample organizers had to choose samples on which the systems were to be tested. A criticism of earlier forays into lexical-sample WSD evaluation is that the lexical sample had been chosen according to the whim of the experimenter (or, to coincide with earlier experimenters' selections). For English Senseval, a sampling frame was devised in which words were classified according to their frequency (in the BNC) and their polysemy level (in WordNet). Also, inclusion POS-tagging problem was a matter of discussion and it was decided that samples should be words with known part of speech and some indeterminants (for ex. 15 noun tasks, 13 verb tasks, 8 adjectives, and 5 indeterminates).

Baselines. For comparison purposes, known, yet simple, algorithms named baselines are used. These include different variants of Lesk algorithm or most frequent sense algorithm.

Sense inventory. WSD exercises require a dictionary, to specify the word senses which are to be disambiguated, and a corpus of language data to be disambiguated. WordNet is the most popular example of sense inventory. The reason for adopting the HECTOR database during Senseval-1 was that the WordNet inventory was already publicly available.^[37]

Evaluation measures. During the evaluation of WSD systems two main performance measures are used:

• Precision: the fraction of system assignments made that are correct
• Recall: the fraction of total word instances correctly assigned by a system

If a system makes an assignment for every word, then precision and recall are the same, and can be called accuracy. This model has been extended to take into account systems that return a set of senses with weights for each occurrence.

Software[link]

• WordNet::SenseRelate, is a project that includes free, open source systems for word sense disambiguation and lexical sample sense disambiguation.
• UKB: Graph Base WSD, is a collection of programs for performing graph-based Word Sense Disambiguation and lexical similarity/relatedness using a pre-existing Lexical Knowledge Base (LKB).

See also[link]

• Ambiguity
• Lesk algorithm
• Lexical substitution
• Part-of-speech tagging
• Polysemy
• Semeval
• Syntactic ambiguity
• Word sense
• Word sense induction

Notes[link]

Works cited[link]

• Agirre, E.; Lopez de Lacalle, A.; Soroa, A. (2009) "Knowledge-based WSD on Specific Domains: Performing better than Generic Supervised WSD" Proc. of IJCAI http://www.ijcai.org/papers09/Papers/IJCAI09-251.pdf 
• Agirre, E.; M. Stevenson. 2006. Knowledge sources for WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY.
• Bar-Hillel, Y. (1964). Language and information. Reading, MA: Addison-Wesley. 
• Buitelaar, P.; B. Magnini, C. Strapparava and P. Vossen. 2006. Domain-specific WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY.
• Chan, Y. S.; H. T. Ng. 2005. Scaling up word sense disambiguation via parallel texts. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI, Pittsburgh, PA).
• Edmonds, P. 2000. Designing a task for SENSEVAL-2. Tech. note. University of Brighton, Brighton. U.K.
• Fellbaum, Christiane (1997) "Analysis of a handwriting task" Proc. of ANLP-97 Workshop on Tagging Text with Lexical Semantics: Why, What, and How? Washington D.C., USA 
• Gliozzo, A.; B. Magnini and C. Strapparava. 2004. Unsupervised domain relevance estimation for word sense disambiguation. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP, Barcelona, Spain).
• Ide, N.; T. Erjavec, D. Tufis. 2002. Sense discrimination with parallel corpora. In Proceedings of ACL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions (Philadelphia, PA).
• Kilgarriff, A. 1997. I don't believe in word senses. Comput. Human. 31(2), pp. 91–113.
• Kilgarriff, A.; G. Grefenstette. 2003. Introduction to the special issue on the Web as corpus. Computational Linguistics 29(3), pp. 333–347
• Kilgarriff, Adam; Joseph Rosenzweig, English Senseval: Report and Results May–June, 2000, University of Brighton
• Lapata, M.; and F. Keller. 2007. An information retrieval approach to sense ranking. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL, Rochester, NY).
• Lenat, D. "Computers versus Common Sense". http://www.youtube.com/watch?v=KSrUHGaUE_c. Retrieved 2008-12-10.  (GoogleTachTalks on YouTube)
• Lenat, D.; R. V. Guha. 1989. Building Large Knowledge-Based Systems, Addison-Wesley
• Lesk; M. 1986. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In Proc. of SIGDOC-86: 5th International Conference on Systems Documentation, Toronto, Canada.
• Litkowski, K. C. 2005. Computational lexicons and dictionaries. In Encyclopaedia of Language and Linguistics (2nd ed.), K. R. Brown, Ed. Elsevier Publishers, Oxford, U.K.
• Magnini, B; G. Cavaglià. 2000. Integrating subject field codes into WordNet. In Proceedings of the 2nd Conference on Language Resources and Evaluation (LREC, Athens, Greece).
• McCarthy, D.; R. Koeling, J. Weeds, J. Carroll. 2007. Unsupervised acquisition of predominant word senses. Computational Linguistics 33(4): 553–590.
• McCarthy, D.; R. Navigli. 2009. The English Lexical Substitution Task, Language Resources and Evaluation, 43(2), Springer.
• Mihalcea, R. 2007. Using Wikipedia for Automatic Word Sense Disambiguation. In Proc. of the North American Chapter of the Association for Computational Linguistics (NAACL 2007), Rochester, April 2007.
• Mohammad, S; G. Hirst. 2006. Determining word sense dominance using a thesaurus. In Proceedings of the 11th Conference on European chapter of the Association for Computational Linguistics (EACL, Trento, Italy).
• Navigli, R. 2006. Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance. Proc. of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics (COLING-ACL 2006), Sydney, Australia.
• Navigli, R.; G. Crisafulli. Inducing Word Senses to Improve Web Search Result Clustering. Proc. of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP 2010), MIT Stata Center, Massachusetts, USA.
• Navigli, R.; M. Lapata. An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 32(4), IEEE Press, 2010.
• Navigli, R.; K. Litkowski, O. Hargraves. 2007. SemEval-2007 Task 07: Coarse-Grained English All-Words Task. Proc. of Semeval-2007 Workshop (SemEval), in the 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007), Prague, Czech Republic.
• Navigli, R.;P. Velardi. 2005. Structural Semantic Interconnections: a Knowledge-Based Approach to Word Sense Disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 27(7).
• Palmer, M.; O. Babko-Malaya and H. T. Dang. 2004. Different sense granularities for different applications. In Proceedings of the 2nd Workshop on Scalable Natural Language Understanding Systems in HLT/NAACL (Boston, MA).
• Ponzetto, S. P.; R. Navigli. Knowledge-rich Word Sense Disambiguation rivaling supervised systems. In Proc. of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), 2010.
• Pradhan, S.; E. Loper, D. Dligach, M. Palmer. 2007. SemEval-2007 Task 17: English lexical sample, SRL and all words. Proc. of Semeval-2007 Workshop (SEMEVAL), in the 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007), Prague, Czech Republic.
• Schütze, H. 1998. Automatic word sense discrimination. Computational Linguistics, 24(1): 97–123.
• Snow, R.; S. Prakash, D. Jurafsky, A. Y. Ng. 2007. Learning to Merge Word Senses, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL).
• Snyder, B.; M. Palmer. 2004. The English all-words task. In Proc. of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3), Barcelona, Spain.
• Weaver, Warren (1949). "Translation". In Locke, W.N.; Booth, A.D.. Machine Translation of Languages: Fourteen Essays. Cambridge, MA: MIT Press. http://www.hutchinsweb.me.uk/MTNI-22-1999.pdf. 
• Wilks, Y.; B. Slator, L. Guthrie. 1996. Electric Words: dictionaries, computers and meanings. Cambridge, MA: MIT Press.
• Yarowsky, D. Word-sense disambiguation using statistical models of Roget's categories trained on large corpora. In Proc. of the 14th conference on Computational linguistics (COLING), 1992.
• Yarowsky, D. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proc. of the 33rd Annual Meeting of the Association for Computational Linguistics.

External links and suggested reading[link]

Look up disambiguation in Wiktionary, the free dictionary.

• Computational Linguistics Special Issue on Word Sense Disambiguation (1998)
• Evaluation Exercises for Word Sense Disambiguation The de-facto standard benchmarks for WSD systems.
• Roberto Navigli. Word Sense Disambiguation: A Survey, ACM Computing Surveys, 41(2), 2009, pp. 1–69. An up-to-date state of the art of the field.
• Word Sense Disambiguation as defined in Scholarpedia
• Word Sense Disambiguation: The State of the Art (PDF) A comprehensive overview By Prof. Nancy Ide & Jean Véronis (1998).
• Word Sense Disambiguation Tutorial, by Rada Mihalcea and Ted Pedersen (2005).
• Word Sense Disambiguation: Algorithms and Applications, edited by Eneko Agirre and Philip Edmonds (2006), Springer. Covers the entire field with chapters contributed by leading researchers. www.wsdbook.org site of the book
• Bar-Hillel, Yehoshua. 1964. Language and Information. New York: Addison-Wesley.
• Edmonds, Philip & Adam Kilgarriff. 2002. Introduction to the special issue on evaluating word sense disambiguation systems. Journal of Natural Language Engineering, 8(4):279-291.
• Edmonds, Philip. 2005. Lexical disambiguation. The Elsevier Encyclopedia of Language and Linguistics, 2nd Ed., ed. by Keith Brown, 607-23. Oxford: Elsevier.
• Ide, Nancy & Jean Véronis. 1998. Word sense disambiguation: The state of the art. Computational Linguistics, 24(1):1-40.
• Jurafsky, Daniel & James H. Martin. 2000. Speech and Language Processing. New Jersey, USA: Prentice Hall.
• Litkowski, K. C. 2005. Computational lexicons and dictionaries. In Encyclopaedia of Language and Linguistics (2nd ed.), K. R. Brown, Ed. Elsevier Publishers, Oxford, U.K., 753–761.
• Manning, Christopher D. & Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press. http://nlp.stanford.edu/fsnlp/
• Mihalcea, Rada. 2007. Word sense disambiguation. Encyclopedia of Machine Learning. Springer-Verlag.
• Resnik, Philip and David Yarowsky. 2000. Distinguishing systems and distinguishing senses: New evaluation methods for word sense disambiguation, Natural Language Engineering, 5(2):113-133. http://www.cs.jhu.edu/~yarowsky/pubs/nle00.ps
• Yarowsky, David. 2001. Word sense disambiguation. Handbook of Natural Language Processing, ed. by Dale et al., 629-654. New York: Marcel Dekker.

The-Dream
The-Dream performing on September 10, 2008 in New York City.
Background information
Birth name	Terius Youngdell Nash
Born	(1977-09-20) September 20, 1977 (age 34) Rockingham, North Carolina, U.S.
Genres	R&B, pop, hip hop
Occupations	singer-songwriter, record producer
Years active	1998–Present
Labels	Def Jam, Radio Killa
Associated acts	Christopher "Tricky" Stewart, Los Da Mystro, Jennifer Lopez, Electrik Red, Rihanna, Kanye West, Beyonce, Jay-Z, Drake Fabolous, Jamie Foxx
Website	the-dreammusic.com

Terius Youngdell Nash (Born September 20, 1977), better known by his stage name The-Dream, is an American singer-songwriter and record producer. He is known for co-writing chart topping hits such as "Baby" for Justin Bieber, "Umbrella" for Rihanna and "Single Ladies (Put a Ring on It)" for Beyoncé, as well as for his three solo albums, Love Hate, Love vs. Money, and Love King.

Early life[link]

Terius Nash was born in Rockingham, North Carolina and moved with his mother, Reva Nash, to Atlanta at the age of 3. He played trumpet, drums, and guitar as a child.^[1] His mother died in 1992 when Nash was fifteen years old, an event which would inspire him to write songs.^[2] He states that the death of his mother gave him a "soft spot" for women, which he credits to his desire to write songs about female empowerment such as Rihanna's "Umbrella".^[2] He moved in with his grandfather, a concrete mason who instilled a strong work ethic in young Nash. Of his grandfather, Nash recalls "He came out of a bad time for blacks in the South, but even though we lived in the hood, we had a boat, some cars and a house that was paid for. So I’ve always had a different outlook on life. There’s nothing I can’t do."^[3]

Music career[link]

Background[link]

Nash met R&B producer Laney Stewart in 2001; Stewart helped him get a publishing deal after writing "Everything" for B2K's album Pandemonium!.^[1] Under the pen name "The-Dream", Nash began writing lyrics for many popular artists. He spent two years working on Nivea's second album Complicated, which he executive produced, and continued to write and produce with Christopher "Tricky" Stewart, Laney's brother, which led to Rihanna's 2007 smash hit "Umbrella". The success of "Umbrella" boosted Dream's music career, and in 2007, he signed a deal with Def Jam Records.^[4]

Solo[link]

Following the deal with Def Jam Records, The-Dream began working on his debut studio album Love Hate.^[4] The album was produced by The-Dream's production partner Tricky Stewart, and Carlos "LOS Da Maestro" McKinney, and featured Fabolous and Rihanna.^[5] The album was written and recorded in eight days with twelve tracks making the final cut. Some of the tracks were written in regards to The-Dream's personal life, most notably "Nikki," written about his ex-wife Nivea and how he has moved on since their divorce.^[6] The-Dream explained the title, Love Hate, as short for, "Love me all summer, hate me all winter, because they love you when you hot and when you're cold they don't... I'm hot right now and they love me, but I was cold and they wasn't fuckin' with me."^[6] Released December 11, 2007 on The-Dream's Def Jam-imprint Radio Killa Records,^[7] Love Hate moved 59,000 copies and debuted at number 30 on the Billboard 200.^[8] Love Hate also peaked at #5 on the Billboard Top R&B/Hip-Hop Albums chart.^[9] The album received generally positive reviews from critics, as Rolling Stone called it "one of the most likable R&B records of the year" and UrbanMusicReviews.com said that the singer has "hit a home run."^[10]

On March 10, 2009 The-Dream released his second album Love vs. Money.^[11] He re-teamed with his production partner Christopher "Tricky" Stewart, who produced most of the tracks on the album, and Los Da Maestro.^[12] He also enlisted the help of Lil Jon for the song "Let Me See the Booty".^[8] Love vs. Money debuted at number two on the Billboard 200, selling 151,000 copies in its first week.^[13] It also entered at number one on Billboard's Top R&B/Hip-Hop Albums and at number three on its Top Digital Albums chart in March 2009.^[14][15] Upon its release, the album received general acclaim from most music critics, based on an aggregate score of 83/100 from Metacritic.^[16]

During the making of Love King, he recorded a song with T-Pain and expressed that he would like to make a collaborative album with Kanye West in the future.^[17] In January 2010, The-Dream officially stated he was finished recording the album and he called it the best of his three albums.^[18] The first single, "Love King", was released on March 16, 2010 for digital download. The album was released on June 29, 2010.^[19]

Before the album's release, the-Dream announced that Love King will be his last solo album.^[8][20] When Love King was released, the lyrics to the song "Sex Intelligent Remix" announced plans to release a new solo album called Love Affair and even gave the specific release date of June 7, 2011. In February 2011, The-Dream announced that the album is not titled "Love Affair" anymore, he stated that it is now titled "The Love, IV" and that he scrapped the June 7, 2011 release date and has set a new release date of August 2011.^[21] One week after anouncing that August release date, he said the he is pushing the album up and is hoping to release it on June 7 as promised.^[22] In June 2011, the album's title was changed yet again, this time to "Love IV: Diary of a Madman", it was also announced that it will released on September 20, 2011.^[23] On June 7, 2011; he released double promotional singles titled "Body Work" and "Fuck My Brains Out" to the internet.^[24] The Dream was not able to release "The Love, IV (Diary of a Mad Man)" on September 20, 2011 as planned and it was announced that the album got pushed back indefinitely due to "contract talks" with Def Jam Records.^[25] The-Dream then released a free 10-track album to the internet entitled 1977 on August 31, 2011.^[26][27] The-Dream also plans to release a 13-track studio album entitled Nikki: The Chronicles, which will follow his infatuation with the woman he sang about on 2007’s Love Hate and 2010’s Love King.^[28] In October 2011, Dream announced that he is personally pushing back the release of The Love; IV (Diary of A Madman) and that it will now be released in 2012. Dream also announced his plans to release his first international album, titled 1984: Persona of Love, it is set for release sometime in 2012, the album is a "persona album" and is inspired by his recent trip overseas.^[29] In February 2012, it was announced that the "The Love; IV (Diary of A Madman)" will be released on April 17, 2012.^[30] In March 2012, it was announced that "The Love; IV (Diary of A Madman)" has been re-titled "Love IV MMXII" and that its release date been pushed back to Summer 2012.^[31] In May 2012, it was announced that the album will be released on August 14, 2012.^[32]

Production[link]

In addition to working with Nivea and Rihanna, The-Dream also began writing for numerous artists, most notably J. Holiday's "Bed", Usher's "Moving Mountains", Yung Joc's "Coffee Shop" and Jesse McCartney's "Leavin'". He also co-wrote Mariah Carey's "Touch My Body", Ciara's "High Price," and took part in co-writing a majority of Mary J. Blige's 2007 Growing Pains album, including the smash hit "Just Fine". Recently^[when?] he featured in Drake (entertainer)'s album Thank Me Later the song "Shut It Down".

In December 2007, The-Dream was nominated for Grammy Award for Song of the Year for "Umbrella", but lost to Amy Winehouse's "Rehab". However, in June 2008, The-Dream won the award for Best New Artist at the 2008 BET Awards. Throughout 2008, The-Dream appeared on a number of records including Plies' "Please Excuse My Hands" with Jamie Foxx, LL Cool J's "Baby", Gym Class Heroes' "Cookie Jar" and Kardinal Offishall's "Gimme Some". In addition, The-Dream signed female R&B girl-group Electrik Red to Radio Killa Records and they released their debut album, How to Be a Lady: Volume 1, under the label.^[33] The-Dream and production partner Christopher "Tricky" Stewart produced "Single Ladies (Put a Ring on It)" off Beyoncé's album I Am… Sasha Fierce, co-produced two tracks ("Just Like Me" & "Digital Girl") and was featured on Jamie Foxx's album Intuition, and worked with Mario and Lionel Richie.^[8] He will also be featured on a number of upcoming albums from; Phyzikal, Ciara, Sterling Simms, Young Chris, Dear Jayne, Brandy and Cassie.

In early 2009, The-Dream began working with Christina Milian on her fourth studio album Elope,^[34] which led to her signing a joint-deal with Radio Killa Records.^[35] The-Dream said that when he was building his label, he went looking for Milian because he believed she was a talented singer.^[36] The singer confirmed that her second single would be a duet with The-Dream called "Supersonic".^[37]

Throughout 2009, The-Dream has also been working on new material for Mariah Carey, Brandy, and Diddy. Diddy's song "Change" is described by The-Dream as, "probably one of the best male records I've ever written...the best record I've written besides 'Umbrella'. The-Dream also worked on Jennifer Lopez's single Louboutins from her 7th album Love? by Sony Music. The song did not chart at the Billboard Hot 100 but charted at number 1 on the Hot/Dance Club Chart. He keep working on more tracks for her album which was released in Spring 2011 by Def Jam.^{[dated info][8][dated info]} He also collaborated on, produced and wrote Snoop Dogg's "Gangsta Luv", which is on the 2009 album Malice n Wonderland.

The-Dream and Tricky Stewart have worked on Ciara's fourth studio album titled "Basic Instinct", which was released on December 14, 2010, and was executive produced by the trio. He co-wrote her lead single titled "Ride", which was released in April 2010, and went on to become a R&B smash hit and her twelfth top ten hit on the Billboard R&B/Hip-Hop Songs chart.^[38]

The-Dream received a fair amount of backlash for his cover of singer Aaliyah's song "One in a Million". Upon the release of the song, fans of Aaliyah bombarded radio shows and blog sites with distaste for The-Dream's rendition.^[39]

He also worked alongside Tricky Stewart on Jessica Simpson's Christmas album titled Happy Christmas, released on November 22, 2010, and also working on Simpson's upcoming pop album, to be released in 2011.^{[dated info]}

The-Dream produced a music single for Kim Kardashian, "Jam (Turn It Up)", released in March 2, 2011 and then worked on Beyoncé's album titled 4, released on June 28, 2011.

On March 16, 2012, Billboard posted an interview with The-Dream, where he talked about having been given the opportunity to go in to the studio in a few weeks to work with Shakira, and how excited he is for the track.^[40]

Personal life[link]

The-Dream married girlfriend Nivea in 2004, whom he had been dating for one year. Together they had three children, daughter Navy Talia (on May 10, 2005); and twin sons London and Christian (April 2006). They had been married for three years when Nivea filed for legal separation in December 2007. The-Dream said that although he was in love with Nivea, his lack of experience in a family growing up meant he was "not taught how much more than love does it take to run a relationship. Like, 'cause love isn't just where it's gonna end. It can't start and stop with love. There has to be a certain amount of knowledge and patience that's acquired in order to keep it going and keep it straight, and I found out the hard way".^[41]

The-Dream began dating Christina Milian in early 2009; she became pregnant. The Daily Mail reported that "the pregnancy was a surprise, but they were getting married regardless".^[42] They then became engaged in June 2009.^[43] In August, it was reported that they had applied for a marriage license in Los Angeles. On September 4, 2009, The-Dream married Christina Milian at the Little White Chapel in Las Vegas.^[44] The-Dream said in an interview that Nivea was still a good friend of his, and that she was also friends with Milian.^[45] Milian gave birth to their daughter Violet Madison Nash on February 26, 2010.^[46] Milian and The-Dream announced their separation on July 12, 2010 ^[47] after photos surfaced of The-Dream vacationing with his assistant.^[48]

In addition to music, The-Dream revealed he is currently working on a TV reality series for MTV where he will scout aspiring singers,^[8] and plans to release a clothing line called Millionaire Love Affair.^[33]

Discography[link]

Studio albums

• Love Hate (2007)
• Love vs. Money (2009)
• Love King (2010)
• Love IV MMXII (2012)

Free album

• 1977 (2011)

Awards and nominations[link]

References[link]

External links[link]

• Official website

The Annoying Orange is an American comedy web series created by Dane Boedigheimer in 2009. It stars Boedigheimer as an anthropomorphic orange who annoys other fruits, vegetables, and various other objects by using crude-humored jokes. Due to the popularity of the show, The Annoying Orange has also become the subject of a TV series, a video game, a range of toys, and a t-shirt line, despite the show's negative reaction. Other accessories, such as costumes of the series characters, have also appeared on the market.

Summary[link]

The show is centered on Orange (voiced by Dane Boedigheimer), who resides on a kitchen counter with other objects such as his best friend Pear, a Bartlett pear (also voiced by Boedigheimer). Other fruits include Passion, a female passionfruit played by Justine Ezarik, grapefruit, a tiny apple known as Midget Apple (though he prefers the name Little Apple), a small marshmallow, and an elderly lemon named Grandpa Lemon, all of whom were not main characters until later episodes. Most episodes consist of Orange heckling other characters until they meet a sudden and gruesome end, usually by evisceration with a chef's knife (although the implements used to cut them up range from a blender to a toy pinwheel). Usually, Orange tries to "warn" them before it happens, blurting out the weapon-in-use, such as "Knife!"^[1]

Orange has several recurring mannerisms. He often begins an episode by repeatedly yelling "Hey, (name of character)!" until the character responds. Orange also often refers to said character as something that plays on the object's name or appearance (such as calling Grapefruit "Apefruit"). If he does not like an object, he will often say, "You're an apple!", even if the object is not (e.g. a tomato). Orange uses various tactics, such as telling rude and offensive jokes, burping, and making childish noises with his tongue, to get attention. He also has a loud, braying laugh.

Characters[link]

Main[link]

• Orange is the lead protagonist (or antagonist, as he literally annoys others until their death) and main character, appearing in every episode since the series began on October 9, 2009. He has yellow teeth, grey eyes, and a braying laugh. He is known for his annoying puns which he uses in every episode, although hardly any of the fruits seem to find his puns amusing. His standard reply, uttered after being taken to task for being so annoying, is, "No I'm not. I'm an orange!"^[1]
• Pear made his initial (albeit brief) appearance in the first episode, before becoming a regular character since the episode "Passion of the Fruit". He is a Bartlett pear who inhabits the same kitchen counter as Orange, his best friend. Despite their friendship, he is often annoyed by Orange, and initially wants to die early on, although he subtly warms up to Orange's antics afterwards. Pear tries to help others maintain control, and is depicted as more intelligent than Orange.
• Passion Fruit (voiced by YouTube vlogger iJustine), or simply "Passion," first appeared on February 19, 2010 in "Passion of the Fruit". Passion is offended by insults, attempts to settle issues, and scolds Orange and other characters. Unbeknownst to Passion, Orange has a crush on her. She has a twin sister named Mandy, who appeared in "Wishful Thinking," and is in love with Pear.
• Midget Apple was first seen in the episode "Crabapple" on August 6, 2010. Also voiced by Boedigheimer, he is a tiny Red Delicious apple. A friend of Orange, Pear, Passion, and especially Marshmallow, he dislikes being called "Midget Apple," preferring the term "'Little Apple."
• Marshmallow debuted in the episode "Annoying Saw 2: The Annoying Death Trap" on September 24, 2010. He is a small, childlike, squeaky-voiced marshmallow that becomes friends with Orange in the episode. Marshmallow is typically presented as cheerful and happy, and claims to love "puppies, and unicorns, and bunnies, and rainbows, and clouds, and kittens, and bunnies, and rainbows." The character is shown to absorb significant damage to his body, from cuts, fire, and throwing stars, but still emerges largely unscathed. Despite his cuteness, size, and cheerfulness, when something or someone angers him, he tends to act destructively, both towards his friends and the area around him. Although his theme song calls him a "he", it was stated by Boedigheimer that it only referred to the term "marshmallow", which is a masculine word, and not the character itself. Thus, Marshmallow's true gender is male, because he has the face of Dane Boedigheimer.
• Grapefruit (voiced by Robert Jennings) first appeared in "Passion of the Fruit" as Orange's archenemy. He often engages in juvenile insults with Orange. The character was killed by Knife in his debut, but became a recurring character after being resurrected as part of the Frankenfruit monster in "Frankenfruit."
• Grandpa Lemon (played by Kevin Brueck) is an elderly, forgetful lemon who has a habit of falling asleep at rather odd and annoying times. First appearing in "Grandpa Lemon" on June 4, 2010, he is unfazed by Orange's taunts about his memory loss and sleeping habits. Sliced in half by Knife in his debut, he became part of the Frankenfruit monster in Frankenfruit, and has become a recurring character ever since.

Recurring[link]

• Knife (voiced by Kevin Nalty) was initially introduced simply as a kitchen tool in "Annoying Orange". He appears when Orange announces his presence, proceeding to slice up the other fruits and vegetables. His first appearance as a character was in "No More Mr. Knife Guy." Knife is shown to be at odds with his role in the series, hating his life because of how he is used. His mortal enemy is a knife sharpener due to the pain involved.
• Liam the Leprechaun (played by Robert Jennings) first appeared in the episode "Luck o' the Irish" as a bad-tempered leprechaun, who loses his pot of gold to Orange. Orange tends to call him "Jolly Green Giant." Crushed by his pot of gold at the end of the episode, Liam is currently a recurring character, and is also the title character of a former spin-off series on his own YouTube channel.^[2] He still continues to return to the Annoying Orange series as a recurring character.
• Squash has appeared in quite a few episodes over 2011-2012. He is similar to Knife, only the fruits get squashed to death, not cut to death. He also appeares as a main fruit in an episode on certain occasions.

Background[link]

Dane Boedigheimer
Born	(1979-09-28) September 28, 1979 (age 32)[3] Harwood, North Dakota
Residence	Los Angeles, California
Nationality	United States of America
Alma mater	University of Minnesota
Occupation	filmmaker, Youtube celebrity
Years active	2004-present
Notable work(s)	The Annoying Orange

Dane Boedigheimer frequently made home videos as a teen.^[4] During his college years, he studied filmmaking in Minnesota, where he met Spencer Grove as an undergraduate.^[5] After finishing college, Boedigheimer and Grove moved to Bakersfield, California and then in 2004 moved to Los Angeles,^[4] where they both worked as production assistants for MTV's Pimp My Ride.^[6] After finishing working for the show, Dane decided that he didn't want to pursue a career in television; "It wasn't me," said Dane. "I wanted to be creative and do things my own way."^[7]

Boedigheimer and his production company, Gagfilms, slowly built a core of fans. In 2006, he opened two Youtube channels named Daneboe and Gagfilms, producing several video series.^[6] Dane's videos have been viewed over 500 million times and have been featured on TV, popular entertainment, news, and video sharing websites.^[8][9] He is also known for his ongoing "Video Game Mashups" series of videos.^[10] In 2010, Boedigheimer's YouTube channel had almost 350 million views and earned an income of $288,000 from ads.^[11]

Before The Annoying Orange, Boedigheimer had done many talking food videos for his channel and other sites including JibJab. He said in an interview that the idea for The Annoying Orange was a combination of the talking food videos, puns and special effects he came up with and did before.^[12] The original video was initially meant to be the only Annoying Orange video on YouTube. However, many viewers requested more videos,^[1][13] and after the 4th one, Boedigheimer decided to make a full-time series. Following the success of the series, he created a channel dedicated to The Annoying Orange under the name "realannoyingorange" on January 11, 2010.

New episodes of The Annoying Orange are released on a weekly basis, with a few exceptions. The episodes are released every Friday on realannoyingorange's YouTube channel.^[1]

Reception[link]

The series was rated as the most viewed web series of February and March 2010 by Mashable, with over 52 million views.^[14][15] On April 26, 2010, the series had over 108 million views on YouTube.^[1][16][17] In June 2010, the channel had received 137 million views.^[13] By August 13, 2010, it received had 1 million subscribers. On June 2011, the channel was ranked as the eighth most subscribed and 30th most viewed, with more than 2,000,000 subscribers.^[18][19] On January 13, 2012, the series hit 1 billion channel views and 2.3 million subscribers.^[20][21]

Despite the show's popularity, it has received some negative critical response. Liz Shannon Miller considered the show to be "annoying for many reasons."^[22] In the web series column Pass the Mustard, Ned Hepburn called the show "pure, pure unfunny, highly concentrated, in droplet form, just purely nonsensical riffing from an Annoying Orange." Hepburn concluded, "the Annoying Orange series is one of the few that I had a physically bad reaction to. It was horrible."^[23] 411mania.com called the show "idiotic" and "creepy as hell",^[24] while other publications have referred to it as "third grade humor."^[1]

Merchandise[link]

Since late 2011, The Collective has produced many accessories, toys and clothing with toymaker The Bridge Direct and clothing retailers such as JCPenney, Shopko and rue21.^[25] On November 10, 2011, GeekDad announced a giveaway for an Annoying Orange T-shirt from JCPenney and a toy from Toys R Us. GeekDad started taking entries through Friday at 11:59 PM.^[26] The Collective also announced a partnership in December of that year with costume manufacturer Rubie's Costume Company to produce children and adult Halloween costumes and accessories featuring characters such as Orange, Pear, Marshmallow, and Midget Apple from the web series.^[27]

Games[link]

A video game developed by Bottle Rocket Apps named Kitchen Carnage was released for the iPod Touch and iPhone on April 7, 2011. The game was later released in HD for the iPad on May 6, 2011, and for Android devices on October 14, 2011. Players throw various produce items across the kitchen at a series of blenders and other kitchen utensils.

TV series[link]

References[link]