Evaluating WordNet-based Measures of Lexical Semantic Relatedness
نویسندگان
چکیده
The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content–based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.
منابع مشابه
WordNet: : Similarity - Measuring the Relatedness of Concepts
WordNet::Similarity is a freely available software package that makes it possible to measure the semantic similarity and relatedness between a pair of concepts (or synsets). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return ...
متن کاملRandom Walk on WordNet to Measure Lexical Semantic Relatedness
The need to determine semantic relatedness or its inverse, semantic distance, between two lexically expressed concepts is a problem that pervades much of natural language processing such as document summarization, information extraction and retrieval, word sense disambiguation and the automatic correction of word errors in text. Standard ways of measuring similarity between two words on a thesa...
متن کاملLexical Semantic Relatedness with Random Graph Walks
Many systems for tasks such as question answering, multi-document summarization, and information retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are based on only a single path between those words in the thesaurus graph. By contrast, we propose a new model of lexical semantic relatedness that incorporates information fro...
متن کاملCombining Word Embedding and Lexical Database for Semantic Relatedness Measurement
While many traditional studies on semantic relatedness utilize the lexical databases, such as WordNet or Wikitionary, the recent word embedding learning approaches demonstrate their abilities to capture syntactic and semantic information, and outperform the lexicon-based methods. However, word senses are not disambiguated in the training phase of both Word2Vec and GloVe, two famous word embeddi...
متن کاملComparing Wikipedia and German Wordnet by Evaluating Semantic Relatedness on Multiple Datasets
We evaluate semantic relatedness measures on different German datasets showing that their performance depends on: (i) the definition of relatedness that was underlying the construction of the evaluation dataset, and (ii) the knowledge source used for computing semantic relatedness. We analyze how the underlying knowledge source influences the performance of a measure. Finally, we investigate th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Computational Linguistics
دوره 32 شماره
صفحات -
تاریخ انتشار 2006