40 citations · 152 across the 36 of their papers we have counts for
5 papers · 1 filter
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi
Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different ta…
Using -way Co-occurrences for Learning Word Embeddings
Danushka Bollegala, Yuichi Yoshida, Ken-ichi Kawarabayashi
Co-occurrences between two words provide useful insights into the semantics of those words. Consequently, numerous prior work on word embedding learning have used co-occurrences be…
Compositional Approaches for Representing Relations Between Words: A Comparative Study
Huda Hakami, Danushka Bollegala
Identifying the relations that exist between words (or entities) is important for various natural language processing tasks such as, relational search, noun-modifier classification…
Learning Neural Word Salience Scores
Krasen Samardzhiev, Andrew Gargett, Danushka Bollegala
Measuring the salience of a word is an essential step in numerous NLP tasks. Heuristic approaches such as tfidf have been used so far to estimate the salience of words. We propose…
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection
Huda Hakami, Danushka Bollegala, Hayashi Kohei
Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, kn…