Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines [PDF]
arXiv, 2017 In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in
arxiv
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks [PDF]
arXiv, 2017 Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artificial neural networks have been recently explored for relation extraction.
arxiv
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing [PDF]
arXiv, 2017 We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based
arxiv
Evaluation of Named Entity Recognition Algorithms in Short Texts
CLEI Electronic Journal, 2017 : One of the major consequences of the growth of social networks has been the generation of huge volumes of content. The text that is generated in social networks constitutes a new type of content, that is short, informal, lacking grammar in some cases,
Edgar Casasola Murillo, Raquel Fonseca
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NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets [PDF]
arXiv, 2020 In this paper, we present the system submitted to "SemEval-2020 Task 12". The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm.
arxiv
Explainable Aspect-Based Sentiment Analysis Using Transformer Models
Big Data and Cognitive ComputingAn 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 +1 more
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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 +2 more
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Multitask Learning with Deep Neural Networks for Community Question Answering
IJCoL, 2017 In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i.e., question-comment similarity, question-question similarity and new question-comment
Daniele Bonadiman +2 more
doaj +1 more source
Duluth at SemEval--2016 Task 14 : Extending Gloss Overlaps to Enrich Semantic Taxonomies [PDF]
arXiv, 2017 This paper describes the Duluth systems that participated in Task 14 of SemEval 2016, Semantic Taxonomy Enrichment. There were three related systems in the formal evaluation which are discussed here, along with numerous post--evaluation runs. All of these systems identified synonyms between WordNet and other dictionaries by measuring the gloss overlaps
arxiv