paper-with-me

Papers

EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering

2017-08-01 · SEMEVAL 2017 8 · Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang, Zhao Lu

We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50{\%}.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Community Question AnsweringFeature EngineeringQuestion AnsweringQuestion Similarity

Similar Papers 제목 키워드 기반

EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction

2018-06-01 · SEMEVAL 2018 6 · Yufei Xie, Qingqing Song

The advent of social media has brought along a novel way of communication where meaning is composed by combining short text messages and visual enhancements, the so-called emojis. We describe our system for participating…

Information RetrievalPredictionSentiment AnalysisTask 2+1

SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering

2021-09-26 · SemEval (ACL) 2016 6 · Tsvetomila Mihaylova, Pepa Gencheva, Martin Boyanov, Ivana Yovcheva 외

We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering. We achieved the best results on subtask C, and strong results on subtasks A and B, by combining a rich set of vario…

Community Question AnsweringQuestion Answering

EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article Similarity

2022-07-01 · SemEval (NAACL) 2022 7 · Elaine Zosa, Emanuela Boros, Boshko Koloski, Lidia Pivovarova

In this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity). We cover several techniques and propose different methods for finding the multilingual n…

ArticlesSemantic SimilaritySemantic Textual SimilaritySentence

EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification

2017-08-01 · SEMEVAL 2017 8 · Maoquan Wang, Shiyun Chen, Yufei Xie, Lu Zhao

This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentime…

ClassificationFeature EngineeringGeneral ClassificationSentence+4

Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection

2020-12-01 · SEMEVAL 2020 · Matej Martinc, Syrielle Montariol, Elaine Zosa, Lidia Pivovarova

This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection. The proposed method is based on clustering of BERT contextual embeddings, foll…

Change DetectionClustering