Results 131 to 140 of about 16,304 (224)

SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions [PDF]

, 2019
Miguel Ángel García Cumbreras, Salud María Jiménez-Zafra, Arturo Montejo‐Ráez, Manuel Carlos Díaz–Galiano, Estela Saquete   +4 more
openalex   +1 more source

Breaking Sticks and Ambiguities with Adaptive Skip-gram

, 2015
Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take
Bartunov, Sergey, Kondrashkin, Dmitry, Osokin, Anton, Vetrov, Dmitry   +3 more
core   +1 more source

CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering [PDF]

, 2019
Adithya Avvaru, Anupam Pandey
openalex   +1 more source

Identifying interactions between chemical entities in biomedical text

Journal of Integrative Bioinformatics, 2014
Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance.
Lamurias Andre, Ferreira João D., Couto Francisco M.   +2 more
doaj   +1 more source

Explainable Aspect-Based Sentiment Analysis Using Transformer Models

Big Data and Cognitive Computing
An aspect-based sentiment analysis (ABSA) aims to perform a fine-grained analysis of text to identify sentiments and opinions associated with specific aspects.
Isidoros Perikos, Athanasios Diamantopoulos   +1 more
doaj   +1 more source

Graph-Based Complex Representation in Inter-Sentence Relation Recognition in Polish Texts

Cybernetics and Information Technologies, 2018
This paper presents a supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. Its core is a graph-based representation constructed for sentences.
Janz Arkadiusz, Kędzia Paweł, Piasecki Maciej   +2 more
doaj   +1 more source

A Hybrid Frequency Based, Syntax, and Conditional Random Field Method for Implicit and Explicit Aspect Extraction

IEEE Access
Aspect extraction is the most important factor influencing the quality of Aspect-Based Sentiment Analysis (ABSA). Aspect extractions are divided into three approaches: supervised, unsupervised, and hybrid methods.
Mohammad Mashrekul Kabir, Zulaiha Ali Othman, Mohd Ridzwan Yaakub   +2 more
doaj   +1 more source

Twitter Sentiment Classification Method Based on Convolutional Neutral Network and Multi-feature Fusion [PDF]

Jisuanji gongcheng, 2018
In order to classify the emotion for users expressions and comments on social networks,this paper presents a sentiment classification method which combines Convolutional Neural Network(CNN) and multi-feature fusion.It designs corpus features and lexicon ...
WANG Rujiao,JI Donghong
doaj  

Design of Intelligent Sentiment Classification Model Based on Deep Neural Network Algorithm in Social Media

IEEE Access
Aspect-based sentiment classification, as a more fine-grained sentiment analysis task, focuses on predicting the sentiment tendency expressed in a sentence based on specific aspects.
Qingxiang Zeng
doaj   +1 more source

ELiRF-UPV at SemEval-2018 Task 10: Capturing Discriminative Attributes with Knowledge Graphs and Wikipedia [PDF]

, 2018
José Ángel González, Lluís-F. Hurtado, Encarna Segarra, Ferran Plà   +3 more
openalex   +1 more source