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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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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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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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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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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Controlled Retrieval Relies on Directed Interactions between Semantic Control Regions and Visual Cortex: MEG Evidence from Oscillatory Dynamics. [PDF]
J NeurosciEisenhauer S +7 more
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Cross-lingual SMS spam detection using GAN-based augmentation for imbalanced datasets. [PDF]
Sci RepFilali A +7 more
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Semantic-aware fault diagnosis of heavy-duty railway maintenance machinery and its potential in multisensor fusion systems. [PDF]
Sci RepZhang Y +8 more
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Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images. [PDF]
ElifeLu Z, Golomb J.
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