Distributional models of word meaning
A Lenci - Annual review of Linguistics, 2018 - annualreviews.org
Distributional semantics is a usage-based model of meaning, based on the assumption that
the statistical distribution of linguistic items in context plays a key role in characterizing their …
the statistical distribution of linguistic items in context plays a key role in characterizing their …
How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings
K Ethayarajh - arXiv preprint arXiv:1909.00512, 2019 - arxiv.org
Replacing static word embeddings with contextualized word representations has yielded
significant improvements on many NLP tasks. However, just how contextual are the …
significant improvements on many NLP tasks. However, just how contextual are the …
Evaluating word embedding models: Methods and experimental results
Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular word …
processing applications is conducted in this work. First, we introduce popular word …
An overview of word and sense similarity
R Navigli, F Martelli - Natural Language Engineering, 2019 - cambridge.org
Over the last two decades, determining the similarity between words as well as between
their meanings, that is, word senses, has been proven to be of vital importance in the field of …
their meanings, that is, word senses, has been proven to be of vital importance in the field of …
Poincar\'e glove: Hyperbolic word embeddings
Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical
structure that the next generation of unsupervised learned word embeddings should reveal …
structure that the next generation of unsupervised learned word embeddings should reveal …
Multimodal distributional semantics
Distributional semantic models derive computational representations of word meaning from
the patterns of co-occurrence of words in text. Such models have been a success story of …
the patterns of co-occurrence of words in text. Such models have been a success story of …
A survey of word embeddings evaluation methods
A Bakarov - arXiv preprint arXiv:1801.09536, 2018 - arxiv.org
Word embeddings are real-valued word representations able to capture lexical semantics
and trained on natural language corpora. Models proposing these representations have …
and trained on natural language corpora. Models proposing these representations have …
[图书][B] Semantic similarity from natural language and ontology analysis
Artificial Intelligence federates numerous scientific fields in the aim of developing machines
able to assist human operators performing complex treatments---most of which demand high …
able to assist human operators performing complex treatments---most of which demand high …
[PDF][PDF] Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.
A Gladkova, A Drozd, S Matsuoka - Proceedings of the NAACL …, 2016 - aclanthology.org
Following up on numerous reports of analogybased identification of “linguistic regularities”
in word embeddings, this study applies the widely used vector offset method to 4 types of …
in word embeddings, this study applies the widely used vector offset method to 4 types of …
Improving hypernymy detection with an integrated path-based and distributional method
Detecting hypernymy relations is a key task in NLP, which is addressed in the literature
using two complementary approaches. Distributional methods, whose supervised variants …
using two complementary approaches. Distributional methods, whose supervised variants …