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We present an approach for the construction of text similarity functions using a parameterized resemblance coefficient in combination with a softened cardinality function called soft cardinality. Our approach provides a consistent and recursive model, varying levels of granularity from sentences to characters. Therefore, our model was used to compare(More)
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 all the task and sub-tasks in which we(More)
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, recording, photocopying, or otherwise, without the prior permission of the publisher. The growth of the amount of available written information originated in the Renaissance with the invention(More)
The use of intelligent systems for stock market predictions has been widely established. This paper introduces a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine,(More)
We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal sentiment analysis of short video clips representing one sentence each. We use the combined feature vectors of textual, visual , and audio modalities to train a(More)
SenticNet is currently one of the most comprehensive freely available semantic resources for opinion mining. However, it only provides numerical polarity scores, while more detailed sentiment-related information for its concepts is often desirable. Another important resource for opinion mining and sentiment analysis is WordNet-Affect, which in turn lacks(More)
For most English words dictionaries give various senses: e.g., " bank " can stand for a financial institution , shore, set, etc. Automatic selection of the sense intended in a given text has crucial importance in many applications of text processing, such as information retrieval or machine translation: e.g., " (my account in the) bank " is to be translated(More)
The task of (monolingual) text alignment consists in finding similar text fragments between two given documents. It has applications in plagiarism detection, detection of text reuse, author identification, authoring aid, and information retrieval, to mention only a few. We describe our approach to the text alignment subtask at the plagiarism detection(More)
The problem of Prepositional Phrase (PP) attachment disambiguation consists in determining if a PP is part of a noun phrase, as in He sees the room with books, or an argument of a verb, as in He fills the room with books. Volk has proposed two variants of a method that queries an Internet search engine to find the most probable attachment variant. In this(More)
A simple representation framework for ontological knowledge with dynamic and deontic characteristics is presented. It represents structural relationships (is-a, part/whole), dynamic relationships (actions such as register, pay, etc.), and conditional relationships (if-then-else). As a case study, we apply our representation language to the task of(More)