[HTML][HTML] A reproducible survey on word embeddings and ontology-based methods for word similarity: Linear combinations outperform the state of the art
Human similarity and relatedness judgements between concepts underlie most of cognitive
capabilities, such as categorisation, memory, decision-making and reasoning. For this …
capabilities, such as categorisation, memory, decision-making and reasoning. For this …
From word to sense embeddings: A survey on vector representations of meaning
Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …
flexible keepers of prior knowledge to be integrated into downstream applications. This …
Counter-fitting word vectors to linguistic constraints
In this work, we present a novel counter-fitting method which injects antonymy and
synonymy constraints into vector space representations in order to improve the vectors' …
synonymy constraints into vector space representations in order to improve the vectors' …
On the limitations of unsupervised bilingual dictionary induction
Unsupervised machine translation---ie, not assuming any cross-lingual supervision signal,
whether a dictionary, translations, or comparable corpora---seems impossible, but …
whether a dictionary, translations, or comparable corpora---seems impossible, but …
Simverb-3500: A large-scale evaluation set of verb similarity
Verbs play a critical role in the meaning of sentences, but these ubiquitous words have
received little attention in recent distributional semantics research. We introduce SimVerb …
received little attention in recent distributional semantics research. We introduce SimVerb …
Semantic specialization of distributional word vector spaces using monolingual and cross-lingual constraints
Abstract 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 …
vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the …
Computer vision and natural language processing: recent approaches in multimedia and robotics
P Wiriyathammabhum, D Summers-Stay… - ACM Computing …, 2016 - dl.acm.org
Integrating computer vision and natural language processing is a novel interdisciplinary field
that has received a lot of attention recently. In this survey, we provide a comprehensive …
that has received a lot of attention recently. In this survey, we provide a comprehensive …
Charagram: Embedding words and sentences via character n-grams
We present Charagram embeddings, a simple approach for learning character-based
compositional models to embed textual sequences. A word or sentence is represented using …
compositional models to embed textual sequences. A word or sentence is represented using …
[HTML][HTML] Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities
Owing to the need for a deep understanding of linguistic items, semantic representation is
considered to be one of the fundamental components of several applications in Natural …
considered to be one of the fundamental components of several applications in Natural …
Semeval-2017 task 2: Multilingual and cross-lingual semantic word similarity
This paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper
introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which …
introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which …