Articles

We present a system for answer ranking (SemEval-2016 Task 3, subtask A) that is a direct adaptation of a pairwise neural net- work model for machine translation evalua- tion (MTE). In particular, the network incor- porates MTE features, as well as rich syntac- tic and semantic embeddings, and it efficiently …...

This paper describes the system we submit- ted to the task 3 (Community Question An- swering) in SemEval 2016, which contains three subtasks, i.e., Question-Comment Simi- larity (subtask A), Question-Question Similar- ity (subtask B), and Question-External Com- ment Similarity (subtask C). For subtask A, we employed three different methods to …...

Resorting to community question answer- ing (CQA) websites for finding answers has gained momentum in the past decade with the explosive rate at which social media has been proliferating. With many ques- tions left unanswered on those websites, auto- matic and smart question answering systems have seen light. One of …...

This paper presents an approach for estimat- ing the question-question similarity of an En- glish dataset specified in Shared Task 3, sub- task B of SemEval-2016. Given a new ques- tion and a set of the first 10 related questions retrieved by a search engine, participants are asked to produce …...

We participate in the English subtask B and C at SemEval-2016 Task 3 “Community Ques- tion Answering”. This paper is concerned with the description of our participating system. We propose a ranking model that combines a trans- lation model with the cosine similarity-based method. Compared to the traditional bag of …...

We describe our submission to SemEval-2016 Task 3 on Community Question Answering. We participated in subtask A, which asks to rerank the comments from the thread for a given forum question from good to bad. Our approach focuses on the generation and use of goodness polarity lexicons, similarly to the …...

This paper describes the results of the partici- pation of The University of Melbourne in the community question-answering (CQA) task of SemEval 2016 (Task 3-B). We obtained a MAP score of 70.2% on the test set, by com- bining three classifiers: a NaiveBayes clas- sifier and a support vector machine …...

We describe our system for finding good an- swers in a community forum, as defined in SemEval-2016, Task 3 on Community Ques- tion Answering. Our approach relies on sev- eral semantic similarity features based on fine- tuned word embeddings and topics similar- ities. In the main Subtask C, our primary …...

We present the system we built for partici- pating in SemEval-2016 Task 3 on Commu- nity Question Answering. We achieved the best results on subtask C, and strong results on subtasks A and B, by combining a rich set of various types of features: semantic, lexical, metadata, and user-related. The …...

Community question answering platforms need to automatically rank answers and ques- tions with respect to a given question. In this paper, we present the approaches for the An- swer Selection and Question Retrieval tasks of SemEval-2016 (task 3). We develop a bag-of-vectors approach with various vector- and text-based features, and …...

Ranking is an important task in the field of in- formation retrieval. Ranking may be used in different modules in natural language pro- cessing such as search engines. In this paper, we introduce a competitive ranking system which combines three different modules. The system participated in SemEval 2016 question ranking …...

In this paper we describe our system (DTSim) submitted at SemEval-2016 Task 2: Inter- pretable Semantic Textual Similarity (iSTS). We participated in both gold chunks cate- gory (texts chunked by human experts and provided by the task organizers) and system chunks category (participants had to automat- ically chunk the input …...

We introduce a system focused on solving SemEval 2016 Task 2 – Interpretable Seman- tic Textual Similarity. The system explores machine learning and rule-based approaches to the task. We focus on machine learning and experiment with a wide variety of ma- chine learning algorithms as well as with sev- eral …...

In our paper we present our rule-based system for semantic processing. In particular we show examples and solutions that may be challenge our approach. We then discuss problems and shortcomings of Task 2 – iSTS. We comment on the existence of a tension between the inherent need to on the …...

Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity. We address various components of this task: chunk level semantic alignment along with assignment of similarity type and score for aligned chunks with a novel system presented in this paper. We propose an algorithm, iMATCH, …...

We present the system developed at FBK for the SemEval 2016 Shared Task 2 ”Inter- pretable Semantic Textual Similarity” as well as the results of the submitted runs. We use a single neural network classification model for predicting the alignment at chunk level, the re- lation type of the alignment …...

In this work we describe the system built for the three English subtasks of the Se- mEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recog- nition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT. …...

We present the description of our submission to SemEval-2016 Task 2, for the sub-task of aligning pre-annotated chunks between sen- tence pairs and providing similarity and relat- edness labels for the alignment. The objective of the task is to provide interpretable seman- tic textual similarity assessments by adding an explanatory …...

This paper describes iUBC, a neural network based approach that achieves competitive re- sults on the interpretable STS task (iSTS 2016). Actually, it achieves top performance in one of the three datasets. iUBC makes use of a jointly trained classifier and regressor, and both models work on top of a …...

This paper describes our system submission to the SemEval 2016 English Semantic Textual Similarity (STS) shared task. The proposed system is based on the compositional text sim- ilarity model, which aggregates pairwise word similarities for computing the semantic simi- larity between texts. In addition, our system combines word importance and …...