Identification of the most frequent sense of a polysemous word is an important semantic task. We introduce two concepts that can benefit MFS detection: companions, which are the most frequently co-occurring words, and the most frequent translation in a bitext. We present two novel methods that incorporate these new concepts, and show that they advance the state of the art on MFS detection.
@cite_20 present a method for based on a thesaurus constructed from a parsed corpus. This thesaurus is used to induce a word similarity function, which they use to assess the prevalence of each sense of a given target word. They perform both intrinsic and extrinsic evaluations; we compare to their reported results to the extent their experimental setup allows. This method was subsequently applied to a WSD shared task @cite_23 , and to the identification of infrequent word senses @cite_12 . An extended analysis of the method was presented by @cite_5 . @cite_39 adapt this method to Japanese using only the glosses of words, excluding the use of semantic networks such as WordNet.
@cite_39 @cite_23 @cite_5 @cite_20 @cite_12
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This paper explores the large-scale acquisition of sense-tagged examples for Word Sense Disambiguation (WSD). We have applied the “WordNet monosemous relatives” method to construct automatically a web corpus that we have used to train disambiguation systems. The corpus-building process has highlighted important factors, such as the distribution of senses (bias). The corpus has been used to train WSD algorithms that include supervised methods (combining automatic and manuallytagged examples), minimally supervised (requiring sense bias information from hand-tagged corpora), and fully unsupervised. These methods were tested on the Senseval-2 lexical sample test set, and compared successfully to other systems with minimum or no supervision. 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. We describe here a method for automatically identifying word sense variation in a dated collection of historical books in a large digital library. By leveraging a small set of known translation book pairs to induce a bilingual sense inventory and labeled training data for a WSD classifier, we are able to automatically classify the Latin word senses in a 389 million word corpus and track the rise and fall of those senses over a span of two thousand years. We evaluate the performance of seven different classifiers both in a tenfold test on 83,892 words from the aligned parallel corpus and on a smaller, manually annotated sample of 525 words, measuring both the overall accuracy of each system and how well that accuracy correlates (via mean square error) to the observed historical variation. 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.