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Bayesian estimation‐based sentiment word embedding model for sentiment analysis
CAAI Transactions on Intelligence Technology, 2022 Sentiment word embedding has been extensively studied and used in sentiment analysis tasks. However, most existing models have failed to differentiate high‐frequency and low‐frequency words.
Jingyao Tang +7 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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Word Embedding as Maximum A Posteriori Estimation [PDF]
, 2019 The GloVe word embedding model relies on solving a global optimization problem, which can be reformulated as a maximum likelihood estimation problem.
Schockart, Steven +5 more
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Compressing Word Embeddings [PDF]
, 2016 10 pages, 0 figures, submitted to ICONIP-2016. Previous experimental results were submitted to ICLR-2016, but the paper has been significantly updated, since a new experimental set-up worked much ...
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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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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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Bias Analysis in Word Embeddings with Alignment Techniques [PDF]
, 2023 openIn the field of Natural Language Processing, word embeddings are fundamental tools to represent the semantic relations among words. These tools are built by training learning algorithms on large corpora of textual data, which often reflect different ...
DELLA CASA, ELENA
core
Citation Intent Classification Using Word Embedding
IEEE Access, 2021 Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations.
Muhammad Roman +4 more
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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 Embedding With Zipf’s Context
IEEE Access, 2019 Word embeddings generated by neural language models have achieved great success in many NLP tasks. However, neural language models may be difficult to train and time consuming.
Lizheng Gao +3 more
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