Word sense

In linguistics, a word sense is one of the meanings of a word. For example, a dictionary may have over 50 different senses of the word play, each of these having a different meaning based on the context of the word's usage in a sentence, as follows:

In each sentence we associate a different meaning of the word "play" based on hints the rest of the sentence gives us.

People and computers, as they read words, must use a process called word-sense disambiguation to find the correct meaning of a word. This process uses context to narrow the possible senses down to the probable ones. The context includes such things as the ideas conveyed by adjacent words and nearby phrases, the known or probable purpose and register of the conversation or document, and the orientation (time and place) implied or expressed. The disambiguation is thus context-sensitive.

A word sense may correspond to either a seme (the smallest unit of meaning) or a sememe (the next larger unit of meaning), and polysemy is the property of having multiple semes or sememes and thus multiple senses.

Word-sense disambiguation

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

The human brain is quite proficient at word-sense disambiguation. The fact that natural language is formed in a way that requires so much of it is a reflection of that neurologic reality. In other words, human language developed in a way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning.

To date, 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.

Natural language processing

Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation.

History

The history of NLP generally starts in the 1950s, although work can be found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence.

The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem. However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. Little further research in machine translation was conducted until the late 1980s, when the first statistical machine translation systems were developed.

Natural language

In neuropsychology, linguistics and the philosophy of language, a natural language or ordinary language is any language that develops naturally in humans through use and repetition (typically, in their first few years of life) without any conscious planning or premeditation of their own. Almost always, therefore, these are the languages human beings use to communicate with each other, whether by speech, signing, touch or writing. They are distinguished from constructed and formal languages such as those used to program computers or to study logic.

Defining natural language

Though the exact definition varies between scholars, natural language can broadly be defined in contrast to artificial or constructed languages (such as computer programming languages and international auxiliary languages) and to other communication systems in nature (such as bees' waggle dance). Definitions of "natural language" also usually state or imply that a "natural" language is one that any cognitively normal human infant is able to learn and whose development has been through use rather than by prescription. An unstandardized language such as African American Vernacular English, for example, is a natural language, whereas a standardized language such as Standard American English is, in part, prescribed.

Computer science

Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information. An alternate, more succinct definition of computer science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems.

Its fields can be divided into a variety of theoretical and practical disciplines. Some fields, such as computational complexity theory (which explores the fundamental properties of computational and intractable problems), are highly abstract, while fields such as computer graphics emphasize real-world visual applications. Still other fields focus on challenges in implementing computation. For example, programming language theory considers various approaches to the description of computation, while the study of computer programming itself investigates various aspects of the use of programming language and complex systems. Human–computer interaction considers the challenges in making computers and computations useful, usable, and universally accessible to humans.