Results 41 to 50 of about 11,306 (258)
A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model
Entropy, 2015 In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word ...
Tao Chen +3 more
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, 2017 In the proceedings of the International Conference on Machine Learning (ICML 2017); 8 pages + references and ...
MANDT STEPHAN MARCEL, BAMLER ROBERT
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Analysis of Italian Word Embeddings [PDF]
, 2017 In this work we analyze the performances of two of the most used word embeddings algorithms, skip-gram and continuous bag of words on Italian language. These algorithms have many hyper-parameter that have to be carefully tuned in order to obtain accurate word representation in vectorial space. We provide an extensive analysis and an evaluation, showing
Tripodi, Rocco, Pira, Stefano Li
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Word Embeddings in Sentiment Analysis [PDF]
, 2018 In the late years sentiment analysis and its applications have reached growing popularity. Concerning this field of research, in the very late years machine learning and word representation learning derived from distributional semantics field (i.e. word embeddings) have proven to be very successful in performing sentiment analysis tasks.
Petrolito R, Dell'Orletta F
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On the Dimensionality of Word Embedding
CoRR, 2018 In this paper, we provide a theoretical understanding of word embedding and its dimensionality. Motivated by the unitary-invariance of word embedding, we propose the Pairwise Inner Product (PIP) loss, a novel metric on the dissimilarity between word embeddings.
Zi Yin, Yuanyuan Shen
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Applied Sciences, 2019 To resolve lexical disagreement problems between queries and frequently asked questions (FAQs), we propose a reliable sentence classification model based on an encoder-decoder neural network.
Youngjin Jang, Harksoo Kim
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Information, 2015 Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts.
Shengyu Liu +3 more
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Towards Resolving Word Ambiguity with Word Embeddings
CoRR, 2023 Ambiguity is ubiquitous in natural language. Resolving ambiguous meanings is especially important in information retrieval tasks. While word embeddings carry semantic information, they fail to handle ambiguity well. Transformer models have been shown to handle word ambiguity for complex queries, but they cannot be used to identify ambiguous words, e.g.
Matthias Thurnbauer +3 more
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Understanding and Creating Word Embeddings
The Programming HistorianWord embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, this lesson will teach you how to prepare a corpus and train a word ...
Avery Blankenship +2 more
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GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts
IEEE Access, 2018 Short texts have become a kind of prevalent source of information, and discovering topical information from short text collections is valuable for many applications.
Wenxin Liang +4 more
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