Articles

This paper describes the SemantiKLUE sys- tem (Proisl et al., 2014) used for the SemEval- 2015 shared task on Semantic Textual Simi- larity (STS) for English. The system was de- veloped for SemEval-2013 and extended for SemEval-2014, where it participated in three tasks and ranked 13th out of 38 submissions …...

This paper describes SVCSTS, a system that was submitted in SemEval-2015 Task 2: Se- mantic Textual Similarity(STS)(Agirre et al., 2015). The task has 3 subtasks viz., English STS, Spanish STS and Interpretable STS. SVCSTS uses Monolingual word aligner (Sul- tan et al., May 2014), supervised machine learning, Google and Bing …...

This paper reports the description and perfor- mance of our system, FBK-HLT, participat- ing in the SemEval 2015, Task #2 “Semantic Textual Similarity”, English subtask. We sub- mitted three runs with different hypothesis in combining typical features (lexical similarity, string similarity, word n-grams, etc) with syn- tactic structure features, resulting …...

This paper describes the system submitted by the University of Wolverhampton and the University of Malaga for SemEval-2015 Task 2: Semantic Textual Similarity. The system uses a Supported Vector Machine approach based on a number of linguistically motivated features. Our system performed satisfactorily for English and obtained a mean 0.7216 …...

Text is composed of words and phrases. In bag-of-word model, phrases in texts are split into words. This may discard the inner seman- tics of phrases which in turn may give incon- sistent relatedness score between two texts. TrWP, the unsupervised text relatedness ap- proach combines both word and phrase …...

This paper presents an approach for estimat- ing the Semantic Textual Similarity of full English sentences as specified in Shared Task 2 of SemEval-2015. The semantic similarity of sentence pairs is quantified from three per- spectives - structural, syntactical, and seman- tic. The numerical representations of the derived similarity measures …...

This paper describes the yiGou system we de- veloped to compute the semantic similarity of two English sentences, which we submitted to the SemEval 2015 Task 2 (English subtask). The system uses a support vector machine model with literal similarity, shallow syntactic similarity, WordNet-based similarity and la- tent semantic similarity …...

In this paper we describe a system that rec- ognizes paraphrases in Twitter for tweets that refer to the same topic. The system partici- pated in Task1 of SEMEVAL-2015 and uses a support vector regression machine to predict the degree of similarity. The similarity is then thresholded to create a …...

When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic simi- larity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines semantic over- …...

This paper describes the USAAR- SHEFFIELD systems that participated in the Semantic Textual Similarity (STS) English task of SemEval-2015. We extend the work on using machine translation evaluation metrics in the STS task. Different from previous approaches, we regard the metrics’ robustness across different text types and conflate the training …...

We present an approach to identifying Twitter paraphrases using simple lexical over- lap features. The work is part of ongoing re- search into the applicability of knowledge- lean techniques to paraphrase identification. We utilize features based on overlap of word and character n-grams and train support vector machine (SVM). Our …...

We describe the system we developed to partic- ipate in SemEval 2015 Task 1, Paraphrase and Semantic Similarity in Twitter. We create simi- larity vectors from two-skip trigrams of prepro- cessed tweets and measure their semantic simi- larity using our UMBC-STS system. We sub- mit two runs. The best result …...

We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Seman- tic Similarity in Twitter. Our paraphrase de- tection system makes use of phrase-structure parse tree embeddings that are then provided as input to a conventional supervised classi- fication model. We achieve an F1 score of 0.45 on …...

This paper describes our approaches to para- phrase recognition in Twitter organized as task 1 in Semantic Evaluation 2015. Lots of ap- proaches have been proposed to address the paraphrasing task on conventional texts ( sur- veyed in (Madnani and Dorr, 2010)). In this work we examined the effectiveness of …...

Paraphrase recognition is the task of iden- tifying whether two pieces of natural lan- guage represent similar meanings. This pa- per describes a system participating in the shared task 1 of SemEval 2015, which is about paraphrase detection and semantic similarity in twitter. Our approach is to exploit se- mantically …...

This paper reports the description and perfor- mance of our system, FBK-HLT, participating in the SemEval 2015, Task #1 "Paraphrase and Semantic Similarity in Twitter", for both sub- tasks. We submitted two runs with different classifiers in combining typical features (lexi- cal similarity, string similarity, word n-grams, etc) with machine …...

This paper describes the system developed by our team (HLTC-HKUST) for task 1 of Se- mEval 2015 workshop about paraphrase clas- sification and semantic similarity in Twitter. We trained a neural network classifier over a range of features that includes translation met- rics, lexical and syntactic similarity score and semantic …...

This paper describes our approach for the Community Question Answering Task, which was presented at the SemEval 2015. The sys- tem should read a given question and identify good, potentially relevant, and bad answers for that question. Our approach transforms the answers of the training set into a graph based …...

This paper describes MITRE’s participation in the Paraphrase and Semantic Similar- ity in Twitter task (SemEval-2015 Task 1). This effort placed first in Semantic Similar- ity and second in Paraphrase Identification with scores of Pearson’s r of 61.9%, F1 of 66.7%, and maxF1 of 72.4%. We detail the approaches we …...

In this shared task, we present evaluations on two related tasks Paraphrase Identification (PI) and Semantic Textual Similarity (SS) sys- tems for the Twitter data. Given a pair of sentences, participants are asked to produce a binary yes/no judgement or a graded score to measure their semantic equivalence. The task …...