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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Joint Word Representation Learning Using a Corpus and a Semantic Lexicon. Bollegala, Danushka; Alsuhaibani, Mohammed; Maehara, Takanori. - : AAAI Press, 2016 BASE Show details 2 Unsupervised Cross-Domain Word Representation Learning ... Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi. - : arXiv, 2015 BASE Show details 3 Joint Word Representation Learning using a Corpus and a Semantic Lexicon ... Bollegala, Danushka; Mohammed, Alsuhaibani; Maehara, Takanori. - : arXiv, 2015 BASE Show details 4 Embedding Semantic Relations into Word Representations ... Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi. - : arXiv, 2015 Abstract: Learning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification. Although there have been several proposals for learning representations for individual words, learning word representations that explicitly capture the semantic relations between words remains under developed. We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words. First, we extract lexical patterns from the co-occurrence contexts of two words in a corpus to represent the semantic relations that exist between those two words. Second, we represent a lexical pattern as the weighted sum of the representations of the words that co-occur with that lexical pattern. Third, we train a binary classifier to detect relationally similar vs. non-similar lexical pattern pairs. The proposed method is unsupervised in the sense that the lexical ... : International Joint Conferences in AI (IJCAI) 2015 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1505.00161 https://dx.doi.org/10.48550/arxiv.1505.00161 BASE Hide details

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