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We address the probhml of automaticMly constructing a thesaurus by clustering words based on corpus data. We view this problem as that of estimating a joint distribution over the (:artesian …...

Latent Semantic Indexing (LSI) approach provides a promising solution to overcome the language barrier between queries and documents, but unfortunately the high dimensions of the training matrix is computationally prohibitive …...

The paper presents a method for word sense disambiguation based on parallel corpora. The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation …...

Word clustering is important for automatic thesaurus construction, text classification, and word sense disambiguation. Recently, several studies have reported using the web as a corpus. This paper proposes an unsupervised …...

. Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks about this model: (1) the problem of word clustering, …...

Word clustering is a conventional and im- portant NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distribu- tional similarity …...

Evaluation of word space models is usually local in the sense that it only considers words that are deemed very similar by the model. We propose a global evaluation scheme …...

This paper explores several unsupervised ap- proaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated part- of-speech (POS) information, …...

Word Sense Disambiguation is the task dedi- cated to the problem of finding out the sense of a word in context, from all of its many pos- sible senses. Solving …...

We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for …...

In this paper, we compare three different generalization methods for in-domain and cross-domain opinion holder extraction be- ing simple unsupervised word clustering, an induction method inspired by distant supervision and …...

Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on rela- tion extraction are mostly supervised. The clear drawback of supervised methods is …...

Automatically clustering words from a mono- lingual or bilingual training corpus into classes is a widely used technique in statisti- cal natural language processing. We present a very simple and …...

Expensive feature engineering based on WordNet senses has been shown be useful for document level sentimentto classification. A plausible reason for such a performance improvement is the reduction in data …...

AbstractThe choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, …...

AbstractThis paper introduces a new method, SummVD, for automatic unsupervised extractive summarization. This method is based on singular value decomposition, a linear method in the number of words, in order …...