Similarity-Based Methods for Word Sense Disambiguation
نویسندگان
چکیده
We compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. The similarity-based methods perform up to 40% better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-based estimates.
منابع مشابه
A new semantic similarity measure evaluated in word sense disambiguation
In this paper, a new conceptual hierarchy based semantic similarity measure is presented, and it is evaluated in word sense disambiguation using a well known algorithm which is called Maximum Relatedness Disambiguation. In this study, WordNet’s conceptual hierarchy is utilized as the data source, but the methods presented are suitable to other resources.
متن کاملEBL-Hope: Multilingual Word Sense Disambiguation Using a Hybrid Knowledge-Based Technique
We present a hybrid knowledge-based approach to multilingual word sense disambiguation using BabelNet. Our approach is based on a hybrid technique derived from the modified version of the Lesk algorithm and the Jiang & Conrath similarity measure. We present our system's runs for the word sense disambiguation subtask of the Multilingual Word Sense Disambiguation and Entity Linking task of SemEva...
متن کاملTiantianzhu7: System Description of Semantic Textual Similarity (STS) in the SemEval-2012 (Task 6)
This paper briefly reports our submissions to the Semantic Textual Similarity (STS) task in the SemEval 2012 (Task 6). We first use knowledge-based methods to compute word semantic similarity as well as Word Sense Disambiguation (WSD). We also consider word order similarity from the structure of the sentence. Finally we sum up several aspects of similarity with different coefficients and get th...
متن کاملSemantic Similarity Functions in Word Sense Disambiguation
This paper presents a method of improving the results of automatic Word Sense Disambiguation by generalizing nouns appearing in a disambiguated context to concepts. A corpus-based semantic similarity function is used for that purpose, by substituting appearances of particular nouns with a set of the most closely related similar words. We show that this approach may be applied to both supervised...
متن کاملNoun Sense Induction and Disambiguation using Graph-Based Distributional Semantics
We introduce an approach to word sense induction and disambiguation. The method is unsupervised and knowledge-free: sense representations are learned from distributional evidence and subsequently used to disambiguate word instances in context. These sense representations are obtained by clustering dependency-based secondorder similarity networks. We then add features for disambiguation from het...
متن کامل