268 citations · 662 across the 69 of their papers we have counts for
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Specialising Word Vectors for Lexical Entailment
Ivan Vulić, Nikola Mrkšić
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entai…
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Ivan Vulić, Nikola Mrkšić, Anna Korhonen
Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In thi…
Decoding Sentiment from Distributed Representations of Sentences
Edoardo Maria Ponti, Ivan Vulić, Anna Korhonen
Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such represen…
A Survey Of Cross-lingual Word Embedding Models
Sebastian Ruder, Ivan Vulić, Anders Søgaard
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural l…
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
Ivan Vulić, Nikola Mrkšić, Roi Reichart +3
Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms;…
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha +5
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the u…