Papers Learning Word Embeddings
“Learning Word Embeddings” 태그가 달린 논문 91편 · 필터 해제
Comply: Learning Sentences with Complex Weights inspired by Fruit Fly Olfaction
Biologically inspired neural networks offer alternative avenues to model data distributions. FlyVec is a recent example that draws inspiration from the fruit fly's olfactory circuit to tackle the task of learning word em…
Computational EfficiencyLearning Word EmbeddingsWord EmbeddingsMulti-Relational Hyperbolic Word Embeddings from Natural Language Definitions
Natural language definitions possess a recursive, self-explanatory semantic structure that can support representation learning methods able to preserve explicit conceptual relations and constraints in the latent space. T…
Learning Word EmbeddingsRepresentation LearningWord EmbeddingsOne Embedder, Any Task: Instruction-Finetuned Text Embeddings
We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike en…
Information RetrievalLearning Word EmbeddingsRetrievalSemantic Textual Similarity+1MorphTE: Injecting Morphology in Tensorized Embeddings
In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is not conducive to the deployment of these …
Learning Word EmbeddingsMachine TranslationQuestion AnsweringTranslation+1HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph
Learning word embeddings is an essential topic in natural language processing. Most existing works use a vast corpus as a primary source while training, but this requires massive time and space for data pre-processing an…
Learning Word EmbeddingsWord EmbeddingsWord SimilarityLeveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations
Word embeddings (Mikolov et al., 2013; Pennington et al., 2014) have been used to bolster the performance of natural language processing systems in a wide variety of tasks, including information retrieval (Roy et al., 20…
Information RetrievalLearning Word EmbeddingsMachine TranslationRetrieval+3ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment
Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data. However, these methods are typi…
Contrastive LearningDomain GeneralizationLearning Word EmbeddingsRepresentation Learning+5Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report
This workshop is the fourth issue of a series of workshops on automatic extraction of socio-political events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of t…
Bias DetectionLearning Word Embeddingstext-classificationText Classification+2Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retail to nursing notes in healthcare. To leverage this information, words are typically translated into…
Generalization BoundsLearning Word EmbeddingsTransfer LearningWord EmbeddingsPoints2Vec: Unsupervised Object-level Feature Learning from Point Clouds
Unsupervised representation learning techniques, such as learning word embeddings, have had a significant impact on the field of natural language processing. Similar representation learning techniques have not yet become…
ClusteringLearning Word EmbeddingsRepresentation LearningWord EmbeddingsTemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing
This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing…
Change DetectionLearning Word EmbeddingsWord EmbeddingsAutomatic Word Association Norms (AWAN)
Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good qua…
Learning Word EmbeddingsReverse DictionaryWord EmbeddingsEmbedding Structured Dictionary Entries
Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the e…
Learning Word EmbeddingsMulti-Task LearningWord EmbeddingsIn Neural Machine Translation, What Does Transfer Learning Transfer?
Transfer learning improves quality for low-resource machine translation, but it is unclear what exactly it transfers. We perform several ablation studies that limit information transfer, then measure the quality impact a…
Learning Word EmbeddingsMachine TranslationTransfer LearningTranslation+1Apprentissage de plongements de mots sur des corpus en langue de sp\'ecialit\'e : une \'etude d'impact (Learning word embeddings on domain specific corpora : an impact study )
Les m{\'e}thodes d{'}apprentissage de plongements lexicaux constituent d{\'e}sormais l{'}{\'e}tat de l{'}art pour la repr{\'e}sentation du vocabulaire et des documents sous forme de vecteurs dans de nombreuses t{\^a}ches…
Learning Word EmbeddingsWord EmbeddingsInfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the model to learning node representations from networ…
Learning Word EmbeddingsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONWord EmbeddingsLearning Cross-Context Entity Representations from Text
Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by…
Entity LinkingLanguage ModelingLanguage ModellingLearning Word Embeddings+2Machine Translation with Cross-lingual Word Embeddings
Learning word embeddings using distributional information is a task that has been studied by many researchers, and a lot of studies are reported in the literature. On the contrary, less studies were done for the case of …
Cross-Lingual Word EmbeddingsLearning Word EmbeddingsMachine TranslationTranslation+1Neural Graph Embedding Methods for Natural Language Processing
Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them. Recent research has resulted in the development of several large KGs. Ho…
Graph EmbeddingKnowledge GraphsLearning Word EmbeddingsLink Prediction+2DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases
The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set. U…
Learning Word EmbeddingsMulti-Label LearningRe-RankingTAG+1