Abstract
BackgroundWord sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context. WSD has been demonstrated to be an important step in knowledge-based approaches to automatic summarization. However, the correlation between the accuracy of the WSD methods and the summarization performance has never been studied.ResultsWe present three existing knowledge-based WSD approaches and a graph-based summarizer. Both the WSD approaches and the summarizer employ the Unified Medical Language System (UMLS) Metathesaurus as the knowledge source. We first evaluate WSD directly, by comparing the prediction of the WSD methods to two reference sets: the NLM WSD dataset and the MSH WSD collection. We next apply the different WSD methods as part of the summarizer, to map documents onto concepts in the UMLS Metathesaurus, and evaluate the summaries that are generated. The results obtained by the different methods in both evaluations are studied and compared.ConclusionsIt has been found that the use of WSD techniques has a positive impact on the results of our graph-based summarizer, and that, when both the WSD and summarization tasks are assessed over large and homogeneous evaluation collections, there exists a correlation between the overall results of the WSD and summarization tasks. Furthermore, the best WSD algorithm in the first task tends to be also the best one in the second. However, we also found that the improvement achieved by the summarizer is not directly correlated with the WSD performance. The most likely reason is that the errors in disambiguation are not equally important but depend on the relative salience of the different concepts in the document to be summarized.
Highlights
Word sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context
Among the unsupervised WSD methods we find journal descriptor indexing (JDI) [22], disambiguation based on concept profiles [23], disambiguation based on context examples collected automatically [24] and graphbased approaches [25]
It must be noted that the Journal Descriptor Indexing (JDI) algorithm performs well with the NLM WSD subset, where all candidate senses of the ambiguous terms are assigned different semantic types, so that JDI is able to distinguish between possible senses
Summary
Word sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context. WSD has been demonstrated to be an important step in knowledgebased approaches to automatic summarization. Word sense disambiguation (WSD) is an open problem of natural language processing (NLP) aimed at resolving lexical ambiguities by identifying the correct meaning of a word based on its context. A word is ambiguous when it has more than one sense (e.g. the word “cold”, when used as a noun, may refer both to a respiratory disorder and to the absence of heat) It is the context in which the word is used that determines its correct meaning. WSD has been demonstrated to be an important step in knowledge-based approaches to automatic summarization [10]. As stated by Shooshan et al [14], the UMLS Metathesaurus contains a significant amount of ambiguity, and selecting the wrong mapping may bias the selection of salient information to sentences containing the wrong concepts, while discarding sentences containing the right ones
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