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20162023
most citedTimeLMs: Diachronic Language Models from Twitter

27 citations · 116 across the 23 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.CL2019

Modelling Semantic Categories using Conceptual Neighborhood

Zied Bouraoui, Jose Camacho-Collados, Luis Espinosa-Anke +1

While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categorie…

cs.CL2019

Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings

Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke +1

Word embeddings have become a standard resource in the toolset of any Natural Language Processing practitioner. While monolingual word embeddings encode information about words in…

cs.CL2019★ 15 cited

On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning

Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke +1

Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the lang…

cs.CL2019

Relational Word Embeddings

Jose Camacho-Collados, Luis Espinosa-Anke, Steven Schockaert

While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In…

cs.CL2019★ 4 cited

Learning Cross-lingual Embeddings from Twitter via Distant Supervision

Jose Camacho-Collados, Yerai Doval, Eugenio Martínez-Cámara +3

Cross-lingual embeddings represent the meaning of words from different languages in the same vector space. Recent work has shown that it is possible to construct such representatio…