Results 101 to 110 of about 33,665 (246)
Automatic Argumentative-Zoning Using Word2vec
, 2017 In comparison with document summarization on the articles from social media and newswire, argumentative zoning (AZ) is an important task in scientific paper analysis. Traditional methodology to carry on this task relies on feature engineering from different levels.
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Investigating Antigram Behaviour using Distributional Semantics
, 2019 Language is an extremely interesting subject to study, each day presenting new challenges and new topics for research. Words in particular have several unique characteristics which when explored, prove to be astonishing.
Sengupta, Saptarshi
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Word2vec Conjecture and A Limitative Result
, 2020 Being inspired by the success of \texttt{word2vec} \citep{mikolov2013distributed} in capturing analogies, we study the conjecture that analogical relations can be represented by vector spaces. Unlike many previous works that focus on the distributional semantic aspect of \texttt{word2vec}, we study the purely \emph{representational} question: can \emph{
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Context-aware Sentiment Analysis on Refined Word Embeddings Word2Vec Model [PDF]
, 2022 A. Sharma
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Sentiment Analysis of Citations Using Word2vec
, 2017 Citation sentiment analysis is an important task in scientific paper analysis. Existing machine learning techniques for citation sentiment analysis are focusing on labor-intensive feature engineering, which requires large annotated corpus. As an automatic feature extraction tool, word2vec has been successfully applied to sentiment analysis of short ...
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Automating the Formation of the Conceptual Structure of the Knowledge Base Using Deep Learning
Кібернетика та комп'ютерні технологіїIntroduction. The ability to automate processes is a key aspect of modern information technology. The construction and use of the conceptual structure of the knowledge base is becoming an urgent need in the modern world, where the amount of information ...
Denys Symonov
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Word and Phrase Translation with word2vec
, 2017 Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements.
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Word2Vec: Um algoritmo saussuriano
This article proposes an interpretation of the functioning of Word2Vec, an algorithm for generating word embeddings, in light of Ferdinand de Saussure’s Theory of Value (TdV). In recent years, Word2Vec has proven highly useful for various NLP tasks—such as text classification, sentiment analysis, and word occurrence probability estimation—due to its ...
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Modeling Musical Context Using Word2vec
, 2017 We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven's piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the
Herremans, Dorien, Chuan, Ching-Hua
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