This paper describes the participation of the HITSZ-ICRC team on the Answer Selection Challenge in SemEval-2015. Our team participated in English subtask A, English subtask B and Arabic task. Two approaches, ensemble learning and hierarchical classification were proposed for answer selection in each task. Bag-of-words features, lexical features and non-textual features were employed. For the Ar...

This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine...

We propose the use of fine-grained part-of-speech (POS) tags as discriminatory attributes for automatic genre classification and report empirical results from an experiment that indicate substantial accuracy gain by such features over the conventional bag-of-words approach through word unigrams. In particular, this paper reports our research to investigate the performance of a fine-grained tag ...

In sentiment analysis of reviews we focus on classifying the polarity (positive, negative) of conveyed opinions from the perspective of textual evidence. Most of the work in the field has been intensively applied on the English language and only few experiments have explored other languages. In this paper, we present a supervised classification of French movie reviews where sentiment analysis i...

In sentiment analysis of reviews we focus on classifying the polarity (positive, negative) of conveyed opinions from the perspective of textual evidence. Most of the work in the field has been intensively applied on the English language and only few experiments have explored other languages. In this paper, we present a supervised classification of French movie reviews where sentiment analysis i...

This research looks at the eeects of word order and segmentation on translation retrieval performance for an experimental Japanese-English translation memory system. We implement a number of both bag-of-words and word order-sensitive similarity metrics, and test each over character-based and word-based indexing. The translation retrieval performance of each system connguration is evaluated empi...

In this paper we describe our participation in the Interactive Social Book Search task at CLEF 2015. We focus our analysis on differences in search behaviour between native and non-native speakers of English. The analysis is based on both questionnaire and log data. 49 participants out of the 192 total participants are native speakers and the remaining 143 participants are nonnative speakers. I...

English. In this work we analyze the performances of two of the most used word embeddings algorithms, skip-gram and continuous bag of words on Italian language. These algorithms have many hyper-parameter that have to be carefully tuned in order to obtain accurate word representation in vectorial space. We provide an accurate analysis and an evaluation, showing what are the best configuration of...

This research looks at the effects of word order and segmentation on translation retrieval performance for an experimental Japanese-English translation memory system. We implement a number of both bag-of-words and word order-sensitive similarity metrics, and test each over characterbased and word-based indexing. The translation retrieval performance of each system configuration is evaluated emp...

This paper proposes a graph-based readability assessment method using word coupling. Compared to the state-of-theart methods such as the readability formulae, the word-based and feature-based methods, our method develops a coupled bag-of-words model which combines the merits of word frequencies and text features. Unlike the general bag-of-words model which assumes words are independent, our mod...