Articles

In this paper, we present the system we have developed for the SemEval-2014 Task 4 dedicated to Aspect-Based Senti- ment Analysis. The system is based on a robust parser that provides information to feed different classifiers with linguis- tic features dedicated to aspect categories and aspect categories polarity classifica- tion. …...

This paper describes the systems submit- ted by the University of San Francisco (USF) to Semeval-2014 Task 4, Aspect Based Sentiment Analysis (ABSA), which provides labeled data in two domains, lap- tops and restaurants. For the constrained condition of both the aspect term extrac- tion and aspect term polarity tasks, …...

We represent natural language semantics by combining logical and distributional in- formation in probabilistic logic. We use Markov Logic Networks (MLN) for the RTE task, and Probabilistic Soft Logic (PSL) for the STS task. The system is evaluated on the SICK dataset. Our best system achieves 73% accuracy on the …...

This paper presents the system submit- ted by University of Wolverhampton for SemEval-2014 task 1. We proposed a ma- chine learning approach which is based on features extracted using Typed Depen- dencies, Paraphrasing, Machine Transla- tion evaluation metrics, Quality Estima- tion metrics and Corpus Pattern Analysis. Our system performed satisfactorily …...

We report results obtained by the UoW method in SemEval-2014’s Task 10 – Mul- tilingual Semantic Textual Similarity. We propose to model Semantic Textual Simi- larity in the context of Multi-task Learning in order to deal with inherent challenges of the task such as unbalanced performance across domains and the …...

In this paper we present our contribution to SemEval-2014 Task 4: Aspect Based Sen- timent Analysis (Pontiki et al., 2014), Sub- task 2: Aspect Term Polarity for Laptop domain. The most outstanding feature in this contribution is the automatic building of a domain-depended sentiment resource using Latent Semantic Analysis. We …...

We give a brief overview of our system, SentiAdaptron, a domain-sensitive and do- main adaptable system for twitter analy- sis in tweets, and discuss performance on SemEval (in both the constrained and un- constrained scenarios), as well as implica- tions arising from comparing the intra- and inter- domain performance on …...

In this paper, the UNITOR system partici- pating in the SemEval-2014 Aspect Based Sentiment Analysis competition is pre- sented. The task is tackled exploiting Ker- nel Methods within the Support Vector Machine framework. The Aspect Term Extraction is modeled as a sequential tag- ging task, tackled through SVMhmm. The Aspect …...

The paper describes our experiments ad- dressing the SemEval 2014 task on the Analysis of Clinical text. Our approach consists in extending the techniques of NE recognition, based on sequence label- ling, to address the special issues of this task, i.e. the presence of overlapping and discontiguous mentions and the …...

This paper describes the UNIBA team participation in the Cross-Level Semantic Similarity task at SemEval 2014. We pro- pose to combine the output of different se- mantic similarity measures which exploit Word Sense Disambiguation and Distribu- tional Semantic Models, among other lex- ical features. The integration of similar- ity measures …...

This paper describes our participation in the SemEval-2014 tasks 1, 3 and 10. We used an uniform approach for addressing all the tasks using the soft cardinality for extracting features from text pairs, and machine learning for predicting the gold standards. Our submitted systems ranked among the top systems in …...

This work describes the participation of the University of Texas Health Science Center at Houston (UTHealth) team on the SemEval 2014 – Task 7 analysis of clinical text challenge. The task consisted of two subtasks: (1) disorder entity recognition, recognizing mentions of disorder concepts; (2) disorder entity encoding, mapping each …...

In this paper we describe our participa- tion in the SemEval 2014, Task 5, con- sisting of the construction of a translation assistance system that translates L1 frag- ments, written in L2 context, to their cor- rect L2 translation. Our approach con- sists of a bilingual parallel corpus, a sys- …...

This work introduces a new approach for aspect based sentiment analysis task. Its main purpose is to automatically assign the correct polarity for the aspect term in a phrase. It is a probabilistic automata where each state consists of all the nouns, adjectives, verbs and adverbs found in an annotated …...

In this paper we describe the specifications and results of UMCC_DLSI system, which was involved in Semeval-2014 addressing two subtasks of Semantic Textual Similarity (STS, Task 10, for English and Spanish), and one subtask of Cross-Level Semantic Similarity (Task 3). As a supervised system, it was provided by different types …...

This paper describes our participation on Task 7 of SemEval 2014, which fo- cused on the recognition and disambigua- tion of medical concepts. We used an adapted version of the Stanford NER sys- tem to train CRF models to recognize tex- tual spans denoting diseases and disor- ders, within clinical …...

We present a sentiment classification sys- tem that participated in the SemEval 2014 shared task on sentiment analysis in Twit- ter. Our system expands tokens in a tweet with semantically similar expressions us- ing a large novel distributional thesaurus and calculates the semantic relatedness of the expanded tweets to word …...

In this paper we introduce our system ca- pable of producing semantic parses of sen- tences using three different annotation for- mats. The system was used to partic- ipate in the SemEval-2014 Shared Task on broad-coverage semantic dependency parsing and it was ranked third with an overall F1-score of 80.49%. …...

We describe our systems for the SemEval 2014 Task 5: L2 writing assistant where a system has to find appropriate translations of L1 segments in a given L2 context. We participated in three out of four possible language pairs (English-Spanish, French- English and Dutch-English) and achieved the best performance for …...

This paper describes our submission to Se- mEval 2014 Task 41 (aspect based senti- ment analysis). The current work is based on the assumption that it could be advan- tageous to connect the subtasks into one workflow, not necessarily following their given order. We took part in all four sub- …...