ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements
نویسندگان
چکیده
This paper reports our submissions to the Semantic Textual Similarity (STS) task in ∗SEM Shared Task 2013. We submitted three Support Vector Regression (SVR) systems in core task, using 6 types of similarity measures, i.e., string similarity, number similarity, knowledge-based similarity, corpus-based similarity, syntactic dependency similarity and machine translation similarity. Our third system with different training data and different feature sets for each test data set performs the best and ranks 35 out of 90 runs. We also submitted two systems in typed task using string based measure and Named Entity based measure. Our best system ranks 5 out of 15 runs.
منابع مشابه
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures
This paper presents our approach used for cross-lingual textual entailment task (task 8) organized within SemEval 2013. Crosslingual textual entailment (CLTE) tries to detect the entailment relationship between two text fragments in different languages. We solved this problem in three steps. Firstly, we use a off-the-shelf machine translation (MT) tool to convert the two input texts into the sa...
متن کاملIHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity
This paper describes the system for rating the degree of semantic equivalence between two text snippets developed by IHS-RD-Belarus for the SemEval 2016 STS shared task (Task 1). To predict the human ratings of text similarity we use a support vector regression model with multiple features representing similarity and difference scores calculated for each
متن کاملTakeLab: Systems for Measuring Semantic Text Similarity
This paper describes the two systems for determining the semantic similarity of short texts submitted to the SemEval 2012 Task 6. Most of the research on semantic similarity of textual content focuses on large documents. However, a fair amount of information is condensed into short text snippets such as social media posts, image captions, and scientific abstracts. We predict the human ratings o...
متن کاملCorpus-based and Knowledge-based Measures of Text Semantic Similarity
This paper presents a method for measuring the semantic similarity of texts, using corpus-based and knowledge-based measures of similarity. Previous work on this problem has focused mainly on either large documents (e.g. text classification, information retrieval) or individual words (e.g. synonymy tests). Given that a large fraction of the information available today, on the Web and elsewhere,...
متن کاملIBM_EG-CORE: Comparing multiple Lexical and NE matching features in measuring Semantic Textual similarity
We present in this paper the systems we participated with in the Semantic Textual Similarity task at SEM 2013. The Semantic Textual Similarity Core task (STS) computes the degree of semantic equivalence between two sentences where the participant systems will be compared to the manual scores, which range from 5 (semantic equivalence) to 0 (no relation). We combined multiple text similarity meas...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013