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 algorithm for word clustering based on a word similarity measure by web counts. Each pair of words is queried to a search engine, which produces a co-occurrence matrix. By cal...

Existing vector space models typically map synonyms and antonyms to similar word vectors, and thus fail to represent antonymy. We introduce a new vector space representation where antonyms lie on opposite sides of a sphere: in the word vector space, synonyms have cosine similarities close to one, while antonyms are close to minus one. We derive this representation with the aid of a thesaurus an...

Resolving coordination ambiguity is a classic hard problem. This paper looks at coordination disambiguation in complex noun phrases (NPs). Parsers trained on the Penn Treebank are reporting impressive numbers these days, but they don’t do very well on this problem (79%). We explore systems trained using three types of corpora: (1) annotated (e.g. the Penn Treebank), (2) bitexts (e.g. Europarl),...

A growing body of research on early word learning suggests that learners gather word-object co-occurrence statistics across learning situations. Here we test a new mechanism whereby learners are also sensitive to word-word co-occurrence statistics. Indeed, we find that participants can infer the likely referent of a novel word based on its co-occurrence with other words, in a way that mimics a ...

One way to analyse word relations is to examine their co-occurrence in the same context. This allows for the identification of potential semantic or lexical relationships between words. As previous studies showed word co-occurrences often reflect human stimuli-response pairs. In this paper significant sentence co-occurrences on word level were used to identify potential responses for word stimu...

This paper presents a novel nonlocal language model which utilizes contextual information. A reduced vector space model calculated from co-occurrences of word pairs provides word co-occurrence vectors. The sum of word cooccurrence vectors represents the context of a document, and the cosine similarity between the context vector and the word co-occurrence vectors represents the long-distance lex...

An experimental approach to studying the properties of word embeddings is proposed. Controlled experiments, achieved through modifications of the training corpus, permit the demonstration of direct relations between word properties and word vector direction and length. The approach is demonstrated using the word2vec CBOW model with experiments that independently vary word frequency and word co-...