Articles

Drug name entity recognition focuses on identifying concepts appearing in the text that correspond to a chemical substance used in pharmacology for treatment, cure, prevention or diagnosis of diseases. This paper describes a system based on ontologies for identifying the chemical substances in biomedical text. The system achieves an F-1 …...

This paper describes the CoMeT system, our contribution to the SemEval 2013 Task 7 chal- lenge, focusing on the task of automatically assessing student answers to factual questions. CoMeT is based on a meta-classifier that uses the outputs of the sub-systems we developed: CoMiC, CoSeC, and three shallower bag ap- …...

Assessing student understanding by evaluat- ing their free text answers to posed questions is a very important task. However, manually, it is time-consuming and computationally, it is difficult. This paper details our shallow NLP approach to computationally assessing student free text answers when a reference answer is provided. For four …...

In this paper, we describe a method for as- sessing student answers, modeled as a para- phrase identification problem, based on sub- stitution by Basic English variants. Basic En- glish paraphrases are acquired from the Sim- ple English Wiktionary. Substitutions are ap- plied both on reference answers and student answers …...

In this paper we describe UMCC_DLSI- (DDI) system which attempts to detect and classify drug entities in biomedical texts. We discuss the use of semantic class and words relevant domain, extracted with ISR- WN (Integration of Semantic Resources based on WordNet) resource to obtain our goal. Following this approach our …...

The domain of DDI identification is constantly showing a rise of interest from scientific community since it represents a decrease of time and healthcare cost. In this paper we purpose a new approach based on shallow linguistic kernel methods to identify DDIs in biomedical manuscripts. The approach outlines a first …...

We present a 5-way supervised system based on syntactic-semantic similarity features. The model deploys: Text overlap measures, WordNet-based lexical similarities, graph- based similarities, corpus-based similarities, syntactic structure overlap and predicate- argument overlap measures. These measures are applied to question, reference answer and student answer triplets. We take into account the …...

In this paper the UNITOR-HMM-TK system participating in the Spatial Role Labeling task at SemEval 2013 is presented. The spatial roles classification is addressed as a sequence-based word classification problem: the SVMhmm learning algorithm is applied, based on a simple feature modeling and a ro- bust lexical generalization achieved through …...

This paper describes the NILC USP system that participated in SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system adopts a hybrid classification process that uses three classification approaches: rule- based, lexicon-based and machine learning approaches. We suggest a pipeline architec- ture that extracts the best characteristics from each classifier. …...

This paper describes the systems with which we participated in the task Sentiment Analysis in Twitter of SEMEVAL 2013 and specifically the Message Polarity Classification. We used a 2-stage pipeline approach employing a lin- ear SVM classifier at each stage and several features including BOW features, POS based features and …...

The widespread use of Twitter makes it very interesting to determine the opinions and the sentiments expressed by its users. The short- ness of the length and the highly informal na- ture of tweets render it very difficult to auto- matically detect such information. This paper reports the results to …...

This paper presents CELI’s participation in the SemEval The Joint Student Response Anal- ysis and 8th Recognizing Textual Entailment Challenge (Task7) and Cross-lingual Textual Entailment for Content Synchronization task (Task 8). 1...

This paper describes the system developed by the Serendio team for the SemEval-2013 Task 2 competition (Task A). We use a lexicon based approach for discovering sentiments. Our lexicon is built from the Serendio tax- onomy. The Serendio taxonomy consists of positive, negative, negation, stop words and phrases. A typical …...

Using labeled Twitter training data from SemEval-2013, we train both a subjectivity classifier and a polarity classifier separately, and then combine the two into a single hier- archical classifier. Using additional unlabeled data that is believed to contain sentiment, we allow the polarity classifier to continue learn- ing using self-training. …...

In this paper, we describe our system sub- mitted for the Sentiment Analysis task at Se- mEval 2013 (Task 2). We implemented a com- bination of Explicit Semantic Analysis (ESA) with Naive Bayes classifier. ESA represents text as a high dimensional vector of explicitly defined topics, following the distributional se- …...

This paper describes an expression-level senti- ment detection system that participated in the subtask A of SemEval-2013 Task 2: Senti- ment Analysis in Twitter. Our system uses a supervised approach to learn the features from the training data to classify expressions in new tweets as positive, negative or neutral. The …...

In this paper, we describe our system for the SemEval-2013 Task 2, Sentiment Analysis in Twitter. We formed features that take into ac- count the context of the expression and take a supervised approach towards subjectivity and polarity classification. Experiments were per- formed on the features to find out whether …...

This paper briefly reports our system for the SemEval-2013 Task 2: sentiment analysis in Twitter. We first used an SVM classifier with a wide range of features, including bag of word features (unigram, bigram), POS fea- tures, stylistic features, readability scores and other statistics of the tweet being analyzed, domain …...

For SemEval-2013 Task 2, A and B (Sen- timent Analysis in Twitter), we use a rule- based pattern matching system that is based on an existing ‘Domain Independent’ sentiment taxonomy for English, essentially a highly phrasal sentiment lexicon. We have made some modifications to our set of rules, based on …...

This paper describes the specifications and re- sults of SSA-UO, unsupervised system, pre- sented in SemEval 2013 for Sentiment Analy- sis in Twitter (Task 2) (Wilson et al., 2013). The proposal system includes three phases: data preprocessing, contextual word polarity detection and message classification. The preprocessing phase comprises treatment of …...