Articles

This paper describes our participation at SemEval- 2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to com- pare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a …...

In this shared task paper for SemEval- 2014 Task 8, we show that most se- mantic structures can be approximated by trees through a series of almost bijective graph transformations. We transform in- put graphs, apply off-the-shelf methods from syntactic parsing on the resulting trees, and retrieve output graphs. Us- …...

In this paper, we develop a deep learn- ing system for message-level Twitter sen- timent classification. Among the 45 sub- mitted systems including the SemEval 2013 participants, our system (Coooolll) is ranked 2nd on the Twitter2014 test set of SemEval 2014 Task 9. Coooolll is built in a supervised learning …...

In this paper, the crucial ingredients for our submission to SemEval-2014 Task 4 “Aspect Level Sentiment Analysis” are discussed. We present a simple aspect de- tection algorithm, a co-occurrence based method for category detection and a dic- tionary based sentiment classification al- gorithm. The dictionary for the latter is based …...

We present two supervised sentiment de- tection systems which were used to com- pete in SemEval-2014 Task 9: Senti- ment Analysis in Twitter. The first sys- tem (Rosenthal and McKeown, 2013) clas- sifies the polarity of subjective phrases as positive, negative, or neutral. It is tai- lored towards online genres, …...

In this paper, we describe our system for the Sentiment Analysis of Twitter shared task in SemEval 2014. Our system uses an SVM classifier along with rich set of lexical features to detect the sentiment of a phrase within a tweet (Task-A) and also the sentiment of the whole tweet …...

We describe the system entered by the National Research Council Canada in the SemEval-2014 L2 writing assistant task. Our system relies on a standard Phrase-Based Statistical Machine Transla- tion trained on generic, publicly available data. Translations are produced by taking the already translated part of the sentence as fixed context. …...

We present an arc-factored statistical model for semantic dependency parsing, as de- fined by the SemEval 2014 Shared Task 8 on Broad-Coverage Semantic Dependency Parsing. Our entry in the open track placed second in the competition. 1...

This article describes a strategy based on a naive-bayes classifier for detecting the po- larity of English tweets. The experiments have shown that the best performance is achieved by using a binary classifier be- tween just two sharp polarity categories: positive and negative. In addition, in or- der to detect …...

This paper describes the two procedures for determining the semantic similarities between sentences submitted for the Se- mEval 2014 Task 1. MeanMaxSim, an unsupervised procedure, is proposed as a new baseline to assess the efficiency gain provided by compositional models. It out- performs a number of other baselines by a …...

We report the results we obtained at the sub- task B (Message Polarity Classification) of Se- mEval 2014 Task 9. The features used for representing the messages were basically tri- grams of characters, trigrams of PoS and a number of words selected by means of a graph mining tool. Our …...

In this paper we present the evaluation of different features for multiligual and cross- level semantic textual similarity. Three dif- ferent types of features were used: lexical, knowledge-based and corpus-based. The re- sults obtained at the Semeval competition rank our approaches above the average of the rest of the teams …...

The results obtained by the BUAP team at Task 1 of SemEval 2014 are presented in this paper. The run submitted is a supervised ver- sion based on two classification models: 1) We used logistic regression for determining the semantic relatedness between a pair of sentences, and 2) We employed …...

The article describes our system submitted to the SemEval-2014 task on Aspect-Based Sentiment Analy- sis. The methods based on distribut- ed representations of words for the aspect term extraction and aspect term polarity detection tasks are pre- sented. The methods for the aspect category detection and category po- larity detection …...

This paper describes the system we sub- mitted to the SemEval-2014 shared task on sentiment analysis in Twitter. Our sys- tem is a hybrid combination of two system developed for a course project at CMU- Qatar. We use an SVM classifier and cou- ple a set of features from one …...

Clinical texts, such as discharge sum- maries or test reports, contain a valuable amount of information that, if efficiently and effectively mined, could be used to infer new knowledge, possibly leading to better diagnosis and therapeutics. With this in mind, the SemEval-2014 Analysis of Clinical Text task aimed at assessing …...

This paper presents the approach of the CISUC-KIS team to the SemEval 2014 task on Sentiment Analysis in Twitter, more precisely subtask B - Message Polar- ity Classification. We followed a machine learning approach where a SVM classifier was trained from a large and diverse set of features that included …...

We describe our approach for the SemEval-2014 task 9: Sentiment Analy- sis in Twitter. We make use of an en- semble learning method for sentiment classification of tweets that relies on varied features such as feature hash- ing, part-of-speech, and lexical fea- tures. Our system was evaluated in the Twitter …...

We describe our approach for the SemEval-2014 task 9: Sentiment Analy- sis in Twitter. We make use of an en- semble learning method for sentiment classification of tweets that relies on varied features such as feature hash- ing, part-of-speech, and lexical fea- tures. Our system was evaluated in the Twitter …...

In this paper, we consider the application of topic modelling to the task of induct- ing grammar rules. In particular, we look at the use of a recently developed method called orthonormal explicit topic analysis, which combines explicit and latent models of semantics. Although, it remains unclear how topic model …...