Articles

This paper is an overview of the system submitted to the SemEval-2014 shared task on sentiment analysis in twitter. For the very first time we participated in both the tasks, viz contextual polarity disam- biguation and message polarity classifi- cation. Our approach is supervised in nature and we use sequential …...

In this paper we report our works for SemEval-2014 Sentiment Analysis Twitter evaluation challenge. This is thein first time we attempt for this task, and our submissions are based on supervised machine learning algorithm. We use Sup- port Vector Machine for both the tasks, viz. contextual polarity disambiguation and message …...

In this paper we briefly describe our super- vised machine learning approach for dis- order mention detection system that we submitted as part of our participation in the SemEval-2014 Shared task. The main goal of this task is to build a system that automatically identifies mentions of clini- cal conditions …...

This paper describes the aspect extraction system submitted by IHS R&D Belarus team at the SemEval-2014 shared task re- lated to Aspect-Based Sentiment Analy- sis. Our system is based on IHS Goldfire linguistic processor and uses a rich set of lexical, syntactic and statistical features in CRF model. We participated …...

This paper describes the HULTECH team par- ticipation in Task 3 of SemEval-2014. Four different subtasks are provided to the partici- pants, who are asked to determine the semantic similarity of cross-level test pairs: paragraph- to-sentence, sentence-to-phrase, phrase-to- word and word-to-sense. Our system adopts a unified strategy (general purpose system) …...

In this paper, we describe our sub- mission to the Shared Task #1. We tried to follow the underlying idea of the task, that is, evaluating the gap of full-fledged recognizing textual en- tailment systems with respect to com- positional distributional semantic mod- els (CDSMs) applied to this task. We …...

In this paper we describe the system sub- mitted for the SemEval 2014 Task 9 (Sen- timent Analysis in Twitter) Subtask B. Our contribution consists of a supervised ap- proach using machine learning techniques, which uses the terms in the dataset as fea- tures. In this work we do not …...

This paper reports the description and scores of our system, FBK-TR, which participated at the SemEval 2014 task #1 "Evaluation of Compositional Distribu- tional Semantic Models on Full Sentences through Semantic Relatedness and Entail- ment". The system consists of two parts: one for computing semantic relatedness, based on SVM, and …...

Recently, the task of measuring seman- tic similarity between given texts has drawn much attention from the Natural Language Processing community. Espe- cially, the task becomes more interesting when it comes to measuring the seman- tic similarity between different-sized texts, e.g paragraph-sentence, sentence-phrase, phrase-word, etc. In this paper, we, the …...

The shared task on Aspect based Senti- ment Analysis primarily focuses on mining relevant information from the thousands of online reviews available for a popular product or service. In this paper we re- port our works on aspect term extraction and sentiment classification with respect to our participation in the …...

This paper presents our approach to se- mantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two ques- tions: (1) Can we solve these two sub- tasks together? (2) Are features proposed for textual entailment task still effective for semantic relatedness task? To …...

This paper describes the system used in Task-7 (Analysis of Clinical Text) of SemEval-2014 for detecting disorder men- tions and associating them with their re- lated CUI of UMLS1. For Task-A, a CRF based sequencing algorithm was used to find different medical entities and a binary SVM classifier was used …...

This paper reports our submissions to the Cross Level Semantic Similarity (CLSS) task in SemEval 2014. We submitted one Random Forest regression system on each cross level text pair, i.e., Paragraph to Sentence (P-S), Sentence to Phrase (S- Ph), Phrase to Word (Ph-W) and Word to Sense (W-Se). For text …...

Microblogging websites (such as Twitter, Facebook) are rich sources of data for opinion mining and sentiment analysis. In this paper, we describe our approaches used for sentiment analysis in twitter (task 9) organized in SemEval 2014. This task tries to determine whether the sentiment orientations conveyed by the whole tweets …...

This paper reports our submissions to the four subtasks of Aspect Based Sentimen- t Analysis (ABSA) task (i.e., task 4) in SemEval 2014 including aspect term ex- traction and aspect sentiment polarity clas- sification (Aspect-level tasks), aspect cat- egory detection and aspect category sen- timent polarity classification (Category- level tasks). …...

This paper describes the Duluth systems that participated in the Cross–Level Se- mantic Similarity task of SemEval–2014. These three systems were all unsupervised and relied on a dictionary melded together from various sources, and used first–order (Lesk) and second–order (Vector) over- laps to measure similarity. The first–order overlaps fared well …...

We present an algorithm for computing the semantic similarity between two sen- tences. It adopts the hypothesis that se- mantic similarity is a monotonically in- creasing function of the degree to which (1) the two sentences contain similar se- mantic units, and (2) such units occur in similar semantic contexts. …...

This paper describes our system used in the Aspect Based Sentiment Analysis Task 4 at the SemEval-2014. Our system con- sists of two components to address two of the subtasks respectively: a Conditional Random Field (CRF) based classifier for Aspect Term Extraction (ATE) and a linear classifier for Aspect Term …...

This paper describes an approach to im- plementing a tool for evaluating seman- tic similarity. We investigated the poten- tial benefits of (1) using text summarisa- tion to narrow down the comparison to the most important concepts in both texts, and (2) leveraging WordNet information to in- crease usefulness of …...

We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submit- ted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity predic- tion), ranking highest out of 36 systems on …...