Articles

Reviews depict sentiments of customers towards various aspects of a product or service. Some of these aspects can be grouped into coarser aspect categories. SemEval-2014 had a shared task (Task 4) on aspect-level sentiment analysis, with over 30 teams participated. In this pa- per, we describe our submissions, which stood …...

This paper describes the NILC USP sys- tem that participated in SemEval-2014 Task 9: Sentiment Analysis in Twitter, a re-run of the SemEval 2013 task under the same name. Our system is an improved version of the system that participated in the 2013 task. This system adopts a hybrid classification …...

This paper details the system NILC USP that participated in the Semeval 2014: As- pect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added se- mantic labels into a basic feature set for measuring the …...

This paper describes our participation in task 7 of SemEval 2014, which focuses on analysis of clinical text. The task is divided into two parts: recognizing men- tions of concepts that belong to the UMLS (Unified Medical Language System) se- mantic group disorders, and mapping each disorder to a unique …...

This paper proposes an approach to solve message- and phrase-level polarity classi- fication in Twitter, derived from an exist- ing system designed for Spanish. As a first step, an ad-hoc preprocessing is per- formed. We then identify lexical, psycho- logical and semantic features in order to capture different dimensions of …...

This paper describes our contribution to the SemEval-2014 Task 9 on sentiment analysis in Twitter. We participated in both strands of the task, viz. classification at message-level (subtask B), and polarity disambiguation of particular text spans within a message (subtask A). Our experi- ments with a variety of lexical and …...

We describe UMBC’s systems developed for the SemEval 2014 tasks on Multi- lingual Semantic Textual Similarity (Task 10) and Cross-Level Semantic Similarity (Task 3). Our best submission in the Multilingual task ranked second in both English and Spanish subtasks using an unsupervised approach. Our best sys- tems for Cross-Level task …...

In this paper, we describe the OPI system participating in the Semeval-2014 task 3 Cross-Level Semantic Similarity. Our ap- proach is knowledge-poor, there is no ex- ploitation of any structured knowledge re- sources as Wikipedia, WordNet or Babel- Net. The method is also fully unsuper- vised, the training set is …...

We turn the Eisner algorithm for parsing to projective dependency trees into a cubic- time algorithm for parsing to a restricted class of directed graphs. To extend the algo- rithm into a data-driven parser, we combine it with an edge-factored feature model and online learning. We report and discuss re- …...

This paper presents the system submitted by KUNLPLab for SemEval-2014 Task9 - Subtask B: Message Polarity on Twitter data. Lexicon features and bag-of-words features are mainly used to represent the datasets. We trained a logistic regression classifier and got an accuracy of 6% in- crease from the baseline feature represen- …...

When executing commands, a robot has a certain level of contextual knowledge about the environment in which it oper- ates. Taking this knowledge into account can be beneficial to disambiguate com- mands with multiple interpretations. We present an approach that uses combina- tory categorial grammars for improving the semantic parsing …...

Sentiment Analysis has become an in- creasingly important research topic. This paper describes our approach to building a system for the Sentiment Analysis in Twit- ter task of the SemEval-2014 evaluation. The goal is to classify a phrase within a short piece of text as positive, negative or neutral. In …...

Text Analytics using semantic information is the latest trend of research due to its potential to represent better the texts content compared with the bag-of-words approaches. On the contrary, representation of semantics through graphs has several advantages over the tradi- tional representation of feature vector. There- fore, error tolerant graph …...

The fast upswing of online reviews and their sentiments on the Web became very useful information to the people. Thus, the opin- ion/sentiment mining has been adopted as a subject of increasingly research interest in the recent years. Being a participant in the Shared Task Challenge, we have developed a …...

In this paper, we describe how we cre- ated a meta-classifier to detect the mes- sage-level sentiment of tweets. We par- ticipated in SemEval-2014 Task 9B by combining the results of several exist- ing classifiers using a random forest. The results of 5 other teams from the competition as well …...

This paper presents the results of the Ix- aMed team at the SemEval-2014 Shared Task 7 on Analyzing Clinical Texts. We have developed three different sys- tems based on: a) exact match, b) a general-purpose morphosyntactic analyzer enriched with the SNOMED CT termi- nology content, and c) a perceptron se- …...

This paper describes the system used by the LIPN team in the task 10, Multilin- gual Semantic Textual Similarity, at Sem- Eval 2014, in both the English and Span- ish sub-tasks. The system uses a sup- port vector regression model, combining different text similarity measures as fea- tures. With respect …...

We describe the Indiana University sys- tem for SemEval Task 5, the L2 writ- ing assistant task, as well as some exten- sions to the system that were completed after the main evaluation. Our team sub- mitted translations for all four language pairs in the evaluation, yielding the top scores …...

This paper describes our approach for the fourth task of the SemEval 2014 challenge: Aspect Based Sentiment Analysis. Our sys- tem is designed to solve all four subtasks: (i) identifying aspect terms, (ii) determin- ing the polarity of an aspect term, (iii) de- tecting aspect categories, and (iv) determin- ing …...

This work analyses various syntactic and lexical features for sentence level aspect based sentiment analysis. The task fo- cuses on detection of a writer’s sentiment towards an aspect which is explicitly men- tioned in a sentence. The target sentiment polarities are positive, negative, conflict and neutral. We use a supervised …...