Word senses are not static and may have temporal, spatial or corpus-specific scopes. Identifying such scopes might benefit the existing WSD systems largely. In this paper, while studying corpus specific word senses, we adapt three existing predominant and novel-sense discovery algorithms to identify these corpus-specific senses. We make use of text data available in the form of millions of digitized books and newspaper archives as two different sources of corpora and propose automated methods to identify corpus-specific word senses at various time points. We conduct an extensive and thorough human judgment experiment to rigorously evaluate and compare the performance of these approaches. Post adaptation, the output of the three algorithms are in the same format and the accuracy results are also comparable, with roughly 45-60 of the reported corpus-specific senses being judged as genuine.
Cook induce word senses and then identify novel senses by comparing two different corpora: the focus corpora' (i.e., a recent version of the corpora) and the reference corpora' (older version of the corpora). Tahmasebi , propose a framework for tracking senses in a newspaper corpus containing articles between 1785 and 1985. Phani study 11 years worth Bengali newswire that allows them to extract trajectories of salient words that are of importance in contemporary West Bengal. Few works @cite_7 @cite_18 have focused on corpus-specific sense identification. One such work is by Dorow and Widdows, where they propose an unsupervised algorithm for automatic corpus-specific sense discovery using a graph based model. McCarthy @cite_0 propose a method to find predominant sense in a corpora. Our work differs from these works in that we capture the cross corpus-specific sense differences by comparing the senses of a particular word obtained across two different corpora. We adapt three state-of-the-art novel and predominant sense discovery algorithms and extensively compare their performances for this task.
@cite_0 @cite_18 @cite_7
2063330778 2121147707 2042675589 1851555520
Distributions of the senses of words are often highly skewed. This fact is exploited by word sense disambiguation (WSD) systems which back off to the predominant sense of a word when contextual clues are not strong enough. The domain of a document has a strong influence on the sense distribution of words, but it is not feasible to produce large manually annotated corpora for every domain of interest. In this paper we describe the construction of three sense annotated corpora in different domains for a sample of English words. We apply an existing method for acquiring predominant sense information automatically from raw text, and for our sample demonstrate that (1) acquiring such information automatically from a mixed-domain corpus is more accurate than deriving it from SemCor, and (2) acquiring it automatically from text in the same domain as the target domain performs best by a large margin. We also show that for an all words WSD task this automatic method is best focussed on words that are salient to the domain, and on words with a different acquired predominant sense in that domain compared to that acquired from a balanced corpus. In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of hand-tagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64 on the nouns of the SENSEVAL-2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domain-specific corpora. In this paper we show that an unsupervised method for ranking word senses automatically can be used to identify infrequently occurring senses. We demonstrate this using a ranking of noun senses derived from the BNC and evaluating on the sense-tagged text available in both SemCor and the SENSEVAL-2 English all-words task. We show that the method does well at identifying senses that do not occur in a corpus, and that those that are erroneously filtered but do occur typically have a lower frequency than the other senses. This method should be useful for word sense disambiguation systems, allowing effort to be concentrated on more frequent senses; it may also be useful for other tasks such as lexical acquisition. Whilst the results on balanced corpora are promising, our chief motivation for the method is for application to domain specific text. For text within a particular domain many senses from a generic inventory will be rare, and possibly redundant. Since a large domain specific corpus of sense annotated data is not available, we evaluate our method on domain-specific corpora and demonstrate that sense types identified for removal are predominantly senses from outside the domain. The unavailability of very large corpora with semantically disambiguated words is a major limitation in text processing research. For example, statistical methods for word sense disambiguation of free text are known to achieve high accuracy results when large corpora are available to develop context rules, to train and test them.This paper presents a novel approach to automatically generate arbitrarily large corpora for word senses. The method is based on (1) the information provided in WordNet, used to formulate queries consisting of synonyms or definitions of word senses, and (2) the information gathered from Internet using existing search engines. The method was tested on 120 word senses and a precision of 91 was observed.