This paper describes the specifications and results of UMCC_DLSI system, which participated in the Semantic Textual Similarity task (STS) of SemEval-2013. Our supervised system uses different types of lexical and semantic features to train a Bagging classifier used to decide the correct option. Related to the different features we can highlight the resource ISR-WN used to extract semantic relat...

In multimedia information retrieval, where a document may contain textual and visual content features, the ranking of documents is often computed by heuristically combining the feature spaces of different media types or combining the ranking scores computed independently from different feature spaces. In this paper, we propose a principled approach inspired by Quantum Theory. Specifically, we p...

In multimedia information retrieval, where a document may contain textual and visual content features, the ranking of documents is often computed by heuristically combining the feature spaces of different media types or combining the ranking scores computed independently from different feature spaces. In this paper, we propose a principled approach inspired by Quantum Theory. Specifically, we p...

We describe the systems submitted by SRI International and the University of the Basque Country for the Semantic Textual Similarity (STS) SemEval-2012 task. Our systems focused on using a simple set of features, featuring a mix of semantic similarity resources, lexical match heuristics, and part of speech (POS) information. We also incorporate precision focused scores over lexical and POS infor...

Over the past few years, collaborative rating sites, such as Netflix, Digg and Stumble, have become increasingly prevalent sites for users to find trending content. I used various data mining techniques to study Digg, a social news site, to examine the influence of content on popularity. What influence does content have on popularity, and what influence does content have on users' decisions? Ov...

Document image processing is a crucial process in the office automation and begins from the ’OCR’ phase with difficulty of the document ’analysis’ and ’understanding’. This paper presents a hybrid and comprehensive approach to document structure analysis. Hybrid in the sense, that it makes use of layout (geometrical) as well as textual features of a given document. These features are the base f...

We describe the Heidelberg University system for the Cross-lingual Textual Entailment task at SemEval-2012. The system relies on features extracted with statistical machine translation methods and tools, combining monolingual and cross-lingual word alignments as well as standard textual entailment distance and bag-of-words features in a statistical learning framework. We learn separate binary c...

This paper advocates a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally decomposable matching score. We argue that the...

This paper describes our Recognizing Textual Entailment (RTE) system developed at University of Ballarat, Australia for participation in the Text Analysis Conference RTE 2010 competition. This year, we participated in the Main task and used a machine learning approach for learning textual entailment relationships using parse-free lexical semantic features. For this, we employed FrameNet and Wor...