Word sense disambiguation

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Word sense disambiguation

What is word sense disambiguation?

In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context, a process that appears to be largely unconscious in people. WSD is a natural classification problem: Given a word and its possible senses, as defined by a dictionary, classify an occurrence of the word in context into one or more of its sense classes. The features of the context (such as neighboring words) provide evidence for classification.

It is an open problem in computational linguistics concerned with identifying which sense of a word is used in a sentence. The solution to this issue impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.

Due to the fact that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's neural networks, computer science has had a long-term challenge in developing the ability in computers to do natural language processing and machine learning.

Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources, supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, and 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.

word sense disambiguation
Source: ResearchGate

What are the approaches and methods of word sense disambiguation?

Approaches and methods of word sense disambiguation

Approaches and methods to WSD are classified according to the source of knowledge used in word disambiguation.

1. Dictionary-based or Knowledge-based Methods

As the name suggests, for disambiguation, these methods primarily rely on dictionaries, treasures and lexical knowledge base. They do not use corpora evidences for disambiguation. The Lesk method is the seminal dictionary-based method introduced by Michael Lesk in 1986. The Lesk definition, on which the Lesk algorithm is based is “measure overlap between sense definitions for all words in context”. However, in 2000, Kilgarriff and Rosensweig gave the simplified Lesk definition as “measure overlap between sense definitions of word and current context”, which further means identify the correct sense for one word at a time. Here the current context is the set of words in surrounding sentence or paragraph.

2. Supervised Methods

For disambiguation, machine learning methods make use of sense-annotated corpora to train. These methods assume that the context can provide enough evidence on its own to disambiguate the sense. In these methods, the words knowledge and reasoning are deemed unnecessary. The context is represented as a set of “features” of the words. It includes the information about the surrounding words also. Support vector machine and memory-based learning are the most successful supervised learning approaches to WSD. These methods rely on substantial amount of manually sense-tagged corpora, which is very expensive to create.

3. Semi-supervised Methods

Due to the lack of training corpus, most of the word sense disambiguation algorithms use semi-supervised learning methods. It is because semi-supervised methods use both labelled as well as unlabeled data. These methods require very small amount of annotated text and large amount of plain unannotated text. The technique that is used by semisupervised methods is bootstrapping from seed data.

4. Unsupervised Methods

These methods assume that similar senses occur in similar context. That is why the senses can be induced from text by clustering word occurrences by using some measure of similarity of the context. This task is called word sense induction or discrimination. Unsupervised methods have great potential to overcome the knowledge acquisition bottleneck due to non-dependency on manual efforts.

What are the applications of word sense disambiguation?

Applications of word sense disambiguation

Word sense disambiguation has many applications in various text processing and NLP fields.

1. Machine Translation

Machine translation or MT is the most obvious application of WSD. In MT, Lexical choice for the words that have distinct translations for different senses, is done by WSD. The senses in MT are represented as words in the target language. Most of the machine translation systems do not use explicit WSD module.

2. Information Retrieval (IR)

Information retrieval (IR) may be defined as a software program that deals with the organization, storage, retrieval and evaluation of information from document repositories particularly textual information. The system basically assists users in finding the information they required but it does not explicitly return the answers of the questions. WSD is used to resolve the ambiguities of the queries provided to IR system. As like MT, current IR systems do not explicitly use WSD module and they rely on the concept that user would type enough context in the query to only retrieve relevant documents.

3. Text Mining and Information Extraction (IE)

In most of the applications, WSD is necessary to do accurate analysis of text. For example, WSD helps intelligent gathering system to do flagging of the correct words. For example, medical intelligent system might need flagging of “illegal drugs” rather than “medical drugs”

4. Lexicography

WSD and lexicography can work together in loop because modern lexicography is corpusbased. With lexicography, WSD provides rough empirical sense groupings as well as statistically significant contextual indicators of sense.

What are the challenges in word sense disambiguation?

WSD faces a lot of challenges and problems.

The most common problem is the difference between various dictionaries or text corpus. Different dictionaries have different meanings for words, which makes the sense of the words to be perceived as different. A lot of text information is out there and often it is not possible to process everything properly.

Different applications need different algorithms and that is often a challenge for WSD.

A problem also arises is that words cannot be divided into discrete meanings. Words often have related meanings and this causes a lot of problems.

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