Abstract | Unsupervised word sense disambiguation (WS D) methods are an attractive approach to all-words WSD due to their non-reliance on expensive annotated data. |
Abstract | Unsupervised estimates of sense frequency have been shown to be very useful for WSD due to the skewed nature of word sense distributions. |
Background and Related Work | There has been a considerable amount of research on representing word senses and disambiguating usages of words in context (WSD) as, in order to produce computational systems that understand and produce natural language, it is essential to have a means of representing and disambiguat-ing word sense . |
Background and Related Work | WSD algorithms require word sense information to disambiguate token instances of a given ambiguous word, e.g. |
Background and Related Work | One extremely useful piece of information is the word sense prior or expected word sense frequency distribution. |
Introduction | The automatic determination of word sense information has been a longterm pursuit of the NLP community (Agirre and Edmonds, 2006; Navigli, 2009). |
Introduction | Word sense distributions tend to be Zip-fian, and as such, a simple but surprisingly high-accuracy back-off heuristic for word sense disambiguation (WSD) is to tag each instance of a given word with its predominant sense (McCarthy et al., 2007). |
Introduction | Such an approach requires knowledge of predominant senses; however, word sense distributions — and predominant senses too —vary from corpus to corpus. |
Abstract | Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search. |
Introduction | Two of the fundamental components of a natural language communication are word sense discovery (Jones, 1986) and word sense disambiguation (Ide and Veronis, 1998). |
Introduction | Context plays a vital role in disambiguation of word senses as well as in the interpretation of the actual meaning of words. |
Introduction | For instance, the word “bank” has several distinct interpretations, including that of a “financial institution” and the “shore of a river.” Automatic discovery and disambiguation of word senses from a given text is an important and challenging problem which has been extensively studied in the literature (Jones, 1986; Ide and Vero-nis, 1998; Schutze, 1998; Navigli, 2009). |
Related work | Word sense disambiguation as well as word sense discovery have both remained key areas of research right from the very early initiatives in natural language processing research. |
Related work | Ide and Vero-nis (1998) present a very concise survey of the history of ideas used in word sense disambiguation; for a recent survey of the state-of-the-art one can refer to (Navigli, 2009). |
Related work | to automatic word sense discovery were made by Karen Sparck Jones (1986); later in lexicography, it has been extensively used as a preprocessing step for preparing mono- and multilingual dictionaries (Kilgarriff and Tugwell, 2001; Kilgarriff, 2004). |
Abstract | In this paper, we propose a sense-based translation model to integrate word senses into statistical machine translation. |
Abstract | Our method is significantly different from preVious word sense disambiguation reformulated for machine translation in that the latter neglects word senses in nature. |
Abstract | Results show that the proposed model substantially outperforms not only the baseline but also the preVious reformulated word sense disambiguation. |
Introduction | Therefore a natural assumption is that word sense disambiguation (WSD) may contribute to statistical machine translation (SMT) by providing appropriate word senses for target translation selection with context features (Carpuat and Wu, 2005). |
Introduction | Carpuat and Wu (2005) adopt a standard formulation of WSD: predicting word senses that are defined on an ontology for ambiguous words. |
Introduction | As they apply WSD to Chinese-to-English translation, they predict word senses from a Chinese ontology HowNet and project the predicted senses to English glosses provided by HowNet. |
Introduction | This word sense issue has been a universal challenge for a range of Natural Language Processing applications, including sentiment analysis. |
Introduction | End-users of such a lexicon may not wish to deal with Word Sense Disam- |
Related Work | There have been recent studies that address word sense disambiguation issues for sentiment analysis. |