Articles

This paper reports the description and perfor- mance of our system, FBK-HLT, participating in the SemEval 2015, Task #3 "Answer Se- lection in Community Question Answering" for English, for both subtasks. We submit two runs with different classifiers in combining typ- ical features (lexical similarity, string similar- ity, word n-grams, …...

This paper describes Al-Bayan team participa- tion in SemEval-2015 Task 3, Subtask A. Task 3 targets semantic solutions for answer selec- tion in community question answering systems. We propose a knowledge-based solution for answer selection of Arabic questions, special- ized for Islamic sciences. We build a Semantic Interpreter to evaluate …...

Answer Validation is an important step in Automatic Question Answering systems and nowadays by spreading Community Question Answering systems it is known as an impor- tant task by itself. Previous works just con- sidered it as a binary classification problem in which they try to find the best answer among …...

In this paper we present the Multilingual All- Words Sense Disambiguation and Entity Link- ing task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known prob- lems in the Natural Language Processing field and both address the lexical ambiguity of lan- guage. Their main difference lies in the kind …...

In this paper, we present a comment labeling system based on a deep learning strategy. We treat the answer selection task as a sequence labeling problem and propose recurrent convolution neural networks to recognize good comments. In the recurrent architecture of our system, our approach uses 2-dimensional convolutional neural networks …...

In this paper, we describe our system for SemEval-2015 Task 3: Answer Selection in Community Question Answering. In this task, the systems are required to identify the good or potentially good answers from the answer thread in Community Question Answering collections. Our system combines 16 features belong to 5 groups …...

This paper describes QCRI’s participation in SemEval-2015 Task 3 “Answer Selection in Community Question Answering”, which tar- geted real-life Web forums, and was offered in both Arabic and English. We apply a super- vised machine learning approach considering a manifold of features including among others word n-grams, text similarity, sentiment …...

This paper describes the participation of the HITSZ-ICRC team on the Answer Selection Challenge in SemEval-2015. Our team parti- cipated in English subtask A, English subtask B and Arabic task. Two approaches, ensemble learning and hierarchical classification were proposed for answer selection in each task. Bag-of-words features, lexical features and …...

In this paper we present an unsupervised ap- proach to word sense disambiguation based on evolutionary game theory. In our algorithm each word to be disambiguated is represented as a node on a graph and each sense as a class. The algorithm performs a consistent class as- signment of senses …...

This paper describes our Align-and- Differentiate approach to the SemEval 2015 Task 2 competition for English Seman- tic Textual Similarity (STS) systems. Our submission achieved the top place on two of the five evaluation datasets. Our team placed 3rd among 28 participating teams, and our three runs ranked 4th, 6th …...

We present in this paper our system developed for SemEval 2015 Shared Task 2 (2a - En- glish Semantic Textual Similarity, STS, and 2c - Interpretable Similarity) and the results of the submitted runs. For the English STS subtask, we used regression models combin- ing a wide array of features …...

In Semantic Textual Similarity, systems rate the degree of semantic equivalence on a graded scale from 0 to 5, with 5 being the most similar. For the English subtask, we present a system which relies on several resources for token-to-token and phrase-to-phrase similarity to build a data-structure which holds all …...

This work uses recursive autoencoders (Socher et al., 2011), word embeddings (Pennington et al., 2014), associative matrices (Schuler, 2014) and lexical overlap features to model human judgments of sentential similarity on SemEval-2015 Task 2: English STS (Agirre et al., 2015). Results show a modest positive correlation between system predictions and …...

This paper describes FCICU team participa- tion in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method us- ing BabelNet relationships. In the English subtask challenge, we submitted three systems (runs) to assess the proposed method. In Run1, we used our proposed …...

We describe a set of top-performing systems at the SemEval 2015 English Semantic Textual Similarity (STS) task. Given two English sen- tences, each system outputs the degree of their semantic similarity. Our unsupervised system, which is based on word alignments across the two input sentences, ranked 5th among 73 sub- …...

We describe the work carried out by the DCU team on the Semantic Textual Similarity task at SemEval-2015. We learn a regression model to predict a semantic similarity score between a sentence pair. Our system exploits distributional semantics in combination with tried-and-tested features from previous tasks in order to compute …...

This paper describes the system used by the LIPN-IIMAS team in the Task 2, Semantic Textual Similarity, at SemEval 2015, in both the English and Spanish sub-tasks. We in- cluded some features based on alignment mea- sures and we tested different learning models, in particular Random Forests, which proved the …...

We present our approach to measuring seman- tic similarity of sentence pairs used in Se- meval 2015 tasks 1 and 2. We adopt the sentence alignment framework of (Han et al., 2013) and experiment with several measures of word similarity. We hybridize the common vector-based models with definition graphs from …...

A typical social networking service contains huge amounts of data, and analyzing this data at the level of the sentence is important. In this paper, we describe our system for a SemEval2015 semantic textual similarity task (task2). We present our approach, which uses edit distance to consider word order, and …...

This paper reports our submissions to seman- tic textual similarity task, i.e., task 2 in Se- mantic Evaluation 2015. We built our sys- tems using various traditional features, such as string-based, corpus-based and syntactic simi- larity metrics, as well as novel similarity mea- sures based on distributed word representa- tions, …...