Articles

In this paper we present our participa- tion in the Semeval 2014 task “Evalu- ation of compositional distributional se- mantic models on full sentences through semantic relatedness and textual entail- ment”. Our results demonstrate that us- ing generic tools for semantic analysis is a viable option for a system that …...

This paper describes the system devel- oped by the UBham team for the SemEval- 2014 Aspect-Based Sentiment Analysis task (Task 4). We present an approach based on deep linguistic processing tech- niques and resources, and explore the pa- rameter space of these techniques applied to the different stages in this …...

This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, com- bining features leveraging word represen- tations with other, more common features, based on word tokens or lexicons. We analyse the contribution of the different features, concluding …...

We describe the grammar induction sys- tem for Spoken Dialogue Systems (SDS) submitted to SemEval’14: Task 2. A sta- tistical model is trained with a rich fea- ture set and used for the selection of can- didate rule fragments. Posterior probabil- ities produced by the fragment selection model are fused …...

This paper describes a system sub- mitted to SemEval-2014 Task 4B: Sentiment Analysis in Twitter, by the team UMCC DLSI Sem integrated by researchers of the University of Matanzas, Cuba and the University of Alicante, Spain. The system adopts a cascade classification process that uses two classi- fiers, K-NN using …...

The TJP system is presented, which partici- pated in SemEval 2014 Task 9, Part A: Contextual Polarity Disambiguation. Our system is ‘constrained’, using only data provided by the organizers. The goal of this task is to identify whether marking contexts are positive, negative or neutral. Our system uses a support …...

In 2014, SemEval organized multiple chal- lenges on natural language processing and information retrieval. One of the task was analysis of the clinical text. This challenge is further divided into two tasks. The task A of the challenge was to extract disor- der mention spans in the clinical text and …...

We present our participation in Task 7 of SemEval shared task 2014. The goal of this particular task includes the identifica- tion of disorder named entities and the mapping of each disorder to a unique Uni- fied Medical Language System concept identifier, which were referred to as Task A and …...

Shared Task 1 of SemEval-2014 com- prised two subtasks on the same dataset of sentence pairs: recognizing textual en- tailment and determining textual similar- ity. We used an existing system based on formal semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in …...

In this paper we describe a Deep Convo- lutional Neural Network (DNN) approach to perform two sentiment detection tasks: message polarity classification and con- textual polarity disambiguation. We apply the proposed approach for the SemEval- 2014 Task 9: Sentiment Analysis in Twit- ter. Despite not using any handcrafted feature or …...

Twitter has become more and more an im- portant resource of user-generated data. Sen- timent Analysis in Twitter is interesting for many applications and objectives. In this pa- per, we propose to exploit some features which can be useful for this task; the main contribution is the use of Z-scores …...

This paper presents an overlap-based ap- proach using bag of words and the Spanish WordNet to solve the STS-Spanish sub- task (STS-Es) of SemEval-2014 Task 10. Since bag of words is the most commonly used method to ascertain similarity, the performance is modest. 1...

This paper describes the system that has been used by TeamX in SemEval-2014 Task 9 Subtask B. The system is a senti- ment analyzer based on a supervised text categorization approach designed with fol- lowing two concepts. Firstly, since lex- icon features were shown to be effective in SemEval-2013 Task …...

This paper presents a word-for-word trans- lation approach using Wiktionary for SemEval-2014 Task 5. The language pairs attempted for this task were English- Spanish and English-German. Since this approach did not take context into account, it performed poorly. 1...

In this paper, we introduce our contribu- tions to the SemEval-2014 Task 4 – As- pect Based Sentiment Analysis (Pontiki et al., 2014) challenge. We participated in the aspect term polarity subtask where the goal was to classify opinions related to a given aspect into positive, negative, neutral or conflict …...

This paper describes the Synalp-Empathic system that competed in SemEval-2014 Task 9B Sentiment Analysis in Twitter. Our system combines syntactic-based va- lence shifting rules with a supervised learning algorithm (Sequential Minimal Optimization). We present the system, its features and evaluate their impact. We show that both the valence shifting mech- …...

We describe a classifier to predict the message-level sentiment of English micro- blog messages from Twitter. This pa- per describes the classifier submitted to the SemEval-2014 competition (Task 9B). Our approach was to build up on the sys- tem of the last year’s winning approach by NRC Canada 2013 (Mohammad …...

In this paper, we present our contribution in SemEval2014 ABSA task, some supervised methods for Aspect-Based Sentiment Analysis of restaurant and laptop reviews are proposed, im- plemented and evaluated. We focus on determin- ing the aspect terms existing in each sentence, finding out their polarities, detecting the catego- ries of …...

This paper provides system description of the cross-level semantic similarity task for the SEMEVAL-2014 workshop. Cross- level semantic similarity measures the de- gree of relatedness between texts of vary- ing lengths such as Paragraph to Sen- tence and Sentence to Phrase. Latent Se- mantic Analysis was used to evaluate the …...

We describe the submission of the team of the Sofia University to SemEval-2014 Task 9 on Sentiment Analysis in Twit- ter. We participated in subtask B, where the participating systems had to predict whether a Twitter message expresses pos- itive, negative, or neutral sentiment. We trained an SVM classifier with …...