text: 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_24: 
  @cite_3: The degree of dominance of a sense of a word is the proportion of occurrences of that sense in text. We propose four new methods to accurately determine word sense dominance using raw text and a published thesaurus. Unlike the (2004) system, these methods can be used on relatively small target texts, without the need for a similarly-sensedistributed auxiliary text. We perform an extensive evaluation using artificially generated thesaurus-sense-tagged data. In the process, we create a word–category cooccurrence matrix, which can be used for unsupervised word sense disambiguation and estimating distributional similarity of word senses, as well.
  @cite_2: 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.
summary: @cite_24 lay the groundwork for by analyzing the relevance of GermaNet synsets to specific domains. @cite_2 build upon this, showing that WSD performance can be improved by performing on a corpus of the same domain as the testing data. @cite_3 present an method based on a published thesaurus, which they use to induce a coarse-grained sense inventory. This separates their method from other related work, which typically uses WordNet as the sense inventory for WSD and .