ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing
نویسندگان
چکیده
Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair. We proposed a new system, ITNLPAiKF, which applies in the SemEval 2017 Task1 Semantic Textual Similarity track 5 English monolingual pairs. In our system, rich features are involved, including Ontology based, word embedding based, Corpus based, Alignment based and Literal based feature. We leveraged the features to predict sentence pair similarity by a Support Vector Regression (SVR) model. In the result, a Pearson Correlation of 0.8231 is achieved by our system, which is a competitive result in the contest of this track.
منابع مشابه
ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository
Community Question Answering (CAQ) systems play an important role in people's lives due to the huge knowledge accumulated in them. In order to take full advantage of the huge knowledge, the target of semeval2016 task3 is to find the best answers to a new question in CQA. This work proposes to use rich semantic text similarity (STS) features to complete the task. We address the task as a ranking...
متن کاملNUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity
We present a multi-feature system for computing the semantic similarity between two sentences. We introduce the use of soft alignment for computing text similarity, and also evaluate different methods to produce it. The main features used by our system are based on alignment and Explicit Semantic Analysis. Our system was above the median scores for 4 out of the 5 datasets at SemEval 2016 STS Ta...
متن کاملUdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features
This paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of a given set of sentence pairs in English. The best run out of three submitted runs of our model achieved a Pearson correlation score of 0.8004 compared to a hidden human annotation of 250 pairs. We used ra...
متن کاملL2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity
This paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Althoug...
متن کاملFCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach
This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, in which a proposed alignment similarity method coupled with new usage of a semantic network (BabelNet) is used. Additionally, a previously proposed integr...
متن کامل