[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
J Camacho-Collados, MT Pilehvar - Journal of Artificial Intelligence …, 2018 - jair.org
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
J Camacho-Collados, MT Pilehvar… - Proceedings of the …, 2017 - aclanthology.org
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 …