Sonia Vázquez

UA-ZBSA: A Headline Emotion Classification through Web Information

This paper presents a headline emotion classification approach based on frequency and co-occurrence information collected from the World Wide Web. The content words of a headline (nouns, verbs, adverbs and adjectives) are extracted in order to form different bag of word pairs with the joy, disgust, fear, anger, sadness and surprise emotions. For each pair, we compute the Mutual Information Score which is obtained from the web occurrences of an emotion and the content words. Our approach is based on the hypothesis that group of words which co-occur together across many documents with a given emotion are highly probable to express the same emotion.

A graph-based approach to WSD using relevant semantic trees and n-cliques model

In this paper we propose a new graph-based approach to solve semantic ambiguity using a semantic net based on WordNet. Our proposal uses an adaptation of the Clique Partitioning Technique to extract sets of strongly related senses. For that, an initial graph is obtained from senses of WordNet combined with the information of several semantic categories from different resources: WordNet Domains, SUMO and WordNet Affect. In order to obtain the most relevant concepts in a sentence we use the Relevant Semantic Trees method. The evaluation of the results has been conducted using the test data set of Senseval-2 obtaining promising results.

UA-ZSA: Web Page Clustering on the basis of Name Disambiguation

This paper presents an approach for web page clustering. The different underlying meanings of a name are discovered on the basis of the title of the web page, the body content, the common named entities across the documents and the sub-links. This information is feeded into a K-Means clustering algorithm which groups together the web pages that refer to the same individual.

A semantic framework for textual data enrichment

A semantic framework for recommender systems is presented.An in-depth analysis of different Natural Language Processing resources is showed.A description of different Natural Language Processing approaches is addressed.Related research works are described.A case of study to evaluate our proposal with real data is presented. In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.

The Usefulness of Conceptual Representation for the Identification of Semantic Variability Expressions

The need of the current Natural Language Processing applications to identify text segments that express the same meaning in different ways, evolved into the identification of semantic variability expressions. Most of the developed approaches focus on the text structure, such as the word overlaps, the distance between phrases or syntactic trees, word to word similarity, logic representation among others. However, current research did not identify how the global conceptual representation of a sentences can contribute to the resolution of this problem. In this paper, we present an approach where the meaning of a sentence is represented with the associated relevant domains. In order to determine the semantic relatedness among text segments, Latent Semantic Analysis is used. We demonstrate, evaluate and analyze the contribution of our conceptual representation approach in an evaluation with the paraphrase task.

“How much context do you need?”: an experiment about the context size in interactive cross-language question answering

The main topic of this paper is the context size needed for an efficient Interactive Cross-language Question Answering system. We compare two approaches: the first one (baseline system) shows the user whole passages (maximum context: 10 sentences). The second one (experimental system) shows only a clause (minimum context). As cross-language system, the main problem is that the language of the question (Spanish) and the language of the answer context (English) are different. The results show that large context is better. However, there are specific relations between the context size and the knowledge about the language of the answer: users with poor level of English prefer context with few words.

Multilingual name disambiguation with semantic information

This paper studies the problem of name ambiguity which concerns the discovery of the different underlying meanings behind a name. We have developed a semantic approach on the basis of which a graph-based clustering algorithm determines the sets of the semantically related sentences that talk about the same name. Our approach is evaluated with the Bulgarian, Romanian, Spanish and English languages for various couples of city, country, person and organization names. The yielded results significantly outperform a majority based classifier and are compared to a bigram co-occurrence approach.

University of Alicante at QA@CLEF2006: answer validation exercise

In this paper we present an Answer Validation System which is based on the combination of word overlap and Latent Semantic Indexing modules. The main contribution of our work consist in the adaptation of our already developed machine-learning textual entailment system MLEnt to the multilingual Answer Validation exercise.

Textual entailment beyond semantic similarity information

The variability of semantic expression is a special characteristic of natural language. This variability is challenging for many natural language processing applications that try to infer the same meaning from different text variants. In order to treat this problem a generic task has been proposed: Textual Entailment Recognition. In this paper, we present a new Textual Entailment approach based on Latent Semantic Indexing (LSI) and the cosine measure. This proposed approach extracts semantic knowledge from different corpora and resources. Our main purpose is to study how the acquired information can be combined with an already developed and tested Machine Learning Entailment system (MLEnt). The experiments show that the combination of MLEnt, LSI and cosine measure improves the results of the initial approach.

Spreading semantic information by Word Sense Disambiguation

Abstract This paper presents an unsupervised approach to solve semantic ambiguity based on the integration of the Personalized PageRank algorithm with word-sense frequency information. Natural Language tasks such as Machine Translation or Recommender Systems are likely to be enriched by our approach, which includes semantic information that obtains the appropriate word-sense via support from two sources: a multidimensional network that includes a set of different resources (i.e. WordNet, WordNet Domains, WordNet Affect, SUMO and Semantic Classes); and the information provided by word-sense frequencies and word-sense collocation from the SemCor Corpus. Our series of results were analyzed and compared against the results of several renowned studies using SensEval-2, SensEval-3 and SemEval-2013 datasets. After conducting several experiments, our procedure produced the best results in the unsupervised procedure category taking SensEval campaigns rankings as reference.