Results 211 to 220 of about 15,278 (236)

Infinite Mixture Chaining: An Efficiency-Based Framework for the Dynamic Construction of Word Meaning. [PDF]

Open Mind (Camb)
Yu L, Xu Y.
europepmc   +1 more source

LERCause: Deep learning approaches for causal sentence identification from nuclear safety reports. [PDF]

PLoS One
Kim J, Kim J, Lee A, Kim J, Diesner J.
europepmc   +1 more source

Evolving techniques in sentiment analysis: a comprehensive review. [PDF]

PeerJ Comput Sci
Kumar M, Khan L, Chang HT.
europepmc   +1 more source

Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need. [PDF]

J Am Med Inform Assoc
Peng C, Yang X, Chen A, Yu Z, Smith KE, Costa AB, Flores MG, Bian J, Wu Y.   +8 more
europepmc   +1 more source

MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification. [PDF]

Heliyon
Agbesi VK, Chen W, Yussif SB, Ukwuoma CC, Gu YH, Al-Antari MA.   +5 more
europepmc   +1 more source

Polytope: High-resolution epitope barcoding forin vivospatial fate-mapping

Postrach D, Pritchard CEJ, Frank L, van Leeuwen T, Messal HA, Krimpenfort P, van Rheenen J, Rodewald H.   +7 more
europepmc   +1 more source

SemEval-2016 Task 4: Sentiment Analysis in Twitter [PDF]

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new
Preslav Nakov, Fabrizio Sebastiani
exaly   +6 more sources

SemEval 2015, Task 7: Diachronic Text Evaluation [PDF]

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
In this paper we describe a novel task, namely the Diachronic Text Evaluation task. A corpus of snippets which contain relevant information for the time when the text was created is extracted from a large collection of newspapers published between 1700 and 2010.
Popescu Octavian, Strapparava Carlo
openaire   +2 more sources

SemEval-2017 Task 4: Sentiment Analysis in Twitter [PDF]

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 2017
sentiment analysis, Twitter, classification, quantification, ranking, English ...
Noura Farra, Preslav Nakov
exaly   +4 more sources