Application of Text Summarization techniques to the Geographical Information Retrieval task

Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field.

How to make a natural language interface to query databases accessible to everyone: An example

Natural Language Interfaces to Query Databases (NLIDBs) have been an active research field since the 1960s. However, they have not been widely adopted. This article explores some of the biggest challenges and approaches for building NLIDBs and proposes techniques to reduce implementation and adoption costs. The article describes {AskMe*}, a new system that leverages some of these approaches and adds an innovative feature: query-authoring services, which lower the entry barrier for end users. Advantages of these approaches are proven with experimentation. Results confirm that, even when {AskMe*} is automatically reconfigurable against multiple domains, its accuracy is comparable to domain-specific NLIDBs.

Tackling redundancy in text summarization through different levels of language analysis

One of the main challenges to be addressed in text summarization concerns the detection of redundant information. This paper presents a detailed analysis of three methods for achieving such goal. The proposed methods rely on different levels of language analysis: lexical, syntactic and semantic. Moreover, they are also analyzed for detecting relevance in texts. The results show that semantic-based methods are able to detect up to 90% of redundancy, compared to only the 19% of lexical-based ones. This is also reflected in the quality of the generated summaries, obtaining better summaries when employing syntactic- or semantic-based approaches to remove redundancy.

Detecting implicit expressions of affect in text using EmotiNet and its extensions

In the past years, an important volume of research in Natural Language Processing has concentrated on the development of automatic systems to deal with affect in text. The different approaches considered dealt mostly with explicit expressions of emotion, at word level. Nevertheless, expressions of emotion are often implicit, inferrable from situations that have an affective meaning. Dealing with this phenomenon requires automatic systems to have “knowledge” on the situation, and the concepts it describes and their interaction, to be able to “judge” it, in the same manner as a person would. This necessity motivated us to develop the EmotiNet knowledge base — a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. In this article, we briefly present the process undergone to build EmotiNet and subsequently propose methods to extend the knowledge it contains. We further on analyse the performance of implicit affect detection using this resource. We compare the results obtained with EmotiNet to the use of alternative methods for affect detection. Following the evaluations, we conclude that the structure and content of EmotiNet are appropriate to address the automatic treatment of implicitly expressed affect, that the knowledge it contains can be easily extended and that overall, methods employing EmotiNet obtain better results than traditional emotion detection approaches.

Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous capabilities whose response times are difficult to predict. The research described in this work aims to respond to this issue by developing a computational model that formalizes the problem and that defines adjusting computing methods. The described proposal combines imprecise computing strategies with cloud computing paradigms in order to provide flexible implementation frameworks for embedded or mobile devices. As a result, the imprecise computation scheduling method on the workload of the embedded system is the solution to move computing to the cloud according to the priority and response time of the tasks to be executed and hereby be able to meet productivity and quality of desired services. A technique to estimate network delays and to schedule more accurately tasks is illustrated in this paper. An application example in which this technique is experimented in running contexts with heterogeneous work loading for checking the validity of the proposed model is described.

COMPENDIUM: una herramienta de generación de resúmenes modular

En este artículo presentamos COMPENDIUM, una herramienta de generación de resúmenes de textos modular. Esta herramienta se compone de un módulo central con cinco etapas bien diferenciadas: i) análisis lingüístico; ii) detección de redundancia; iii) identificación del tópico; iv) detección de relevancia; y v) generación del resumen, y una serie de módulos adicionales que permiten incrementar las funcionalidades de la herramienta permitiendo la generación de distintos tipos de resúmenes, como por ejemplo orientados a un tema concreto. Realizamos una evaluación exhaustiva en dos dominios distintos (noticias de prensa y documentos sobre lugares turísticos) y analizamos diferentes tipos de resúmenes generados con COMPENDIUM (mono-documento, multi-documento, genéricos y orientados a un tema). Además, comparamos nuestro sistema con otros sistemas de generación de resúmenes actuales. Los resultados que se obtienen demuestran que la herramienta COMPENDIUM es capaz de generar resúmenes competitivos para los distintos tipos de resúmenes propuestos.

Enriching the integration of semantic resources based on WordNet

In this paper we present the enrichment of the Integration of Semantic Resources based in WordNet (ISR-WN Enriched). This new proposal improves the previous one where several semantic resources such as SUMO, WordNet Domains and WordNet Affects were related, adding other semantic resources such as Semantic Classes and SentiWordNet. Firstly, the paper describes the architecture of this proposal explaining the particularities of each integrated resource. After that, we analyze some problems related to the mappings of different versions and how we solve them. Moreover, we show the advantages that this kind of tool can provide to different applications of Natural Language Processing. Related to that question, we can demonstrate that the integration of semantic resources allows acquiring a multidimensional vision in the analysis of natural language.

Procesamiento automático de metáforas con métodos no supervisados

Proyecto emergente centrado en la detección e interpretación de metáforas con métodos no supervisados. Se presenta la caracterización del problema metafórico en Procesamiento del Lenguaje Natural, los fundamentos teóricos del proyecto y los primeros resultados.

An approach based on Logic Forms and WordNet relationships to Textual Entailment performance

This paper outlines the approach adopted by the PLSI research group at University of Alicante in the PASCAL-2006 second Recognising Textual Entailment challenge. Our system is composed of several components. On the one hand, the first component performs the derivation of the logic forms of the text/hypothesis pairs and, on the other hand, the second component provides us with a similarity score given by the semantic relations between the derived logic forms. In order to obtain this score we apply several measures of similitude and relatedness based on the structure and content of WordNet.

The OpAL System at NTCIR 8 MOAT

The present is marked by the availability of large volumes of heterogeneous data, whose management is extremely complex. While the treatment of factual data has been widely studied, the processing of subjective information still poses important challenges. This is especially true in tasks that combine Opinion Analysis with other challenges, such as the ones related to Question Answering. In this paper, we describe the different approaches we employed in the NTCIR 8 MOAT monolingual English (opinionatedness, relevance, answerness and polarity) and cross-lingual English-Chinese tasks, implemented in our OpAL system. The results obtained when using different settings of the system, as well as the error analysis performed after the competition, offered us some clear insights on the best combination of techniques, that balance between precision and recall. Contrary to our initial intuitions, we have also seen that the inclusion of specialized Natural Language Processing tools dealing with Temporality or Anaphora Resolution lowers the system performance, while the use of topic detection techniques using faceted search with Wikipedia and Latent Semantic Analysis leads to satisfactory system performance, both for the monolingual setting, as well as in a multilingual one.

Evaluating the robustness of EmotiBlog for sentiment analysis and opinion mining

Preliminary research demonstrated the EmotiBlog annotated corpus relevance as a Machine Learning resource to detect subjective data. In this paper we compare EmotiBlog with the JRC Quotes corpus in order to check the robustness of its annotation. We concentrate on its coarse-grained labels and carry out a deep Machine Learning experimentation also with the inclusion of lexical resources. The results obtained show a similarity with the ones obtained with the JRC Quotes corpus demonstrating the EmotiBlog validity as a resource for the SA task.

PHORA: a system to solve the anaphora in Spanish

In this paper we present a whole Natural Language Processing (NLP) system for Spanish. The core of this system is the parser, which uses the grammatical formalism Lexical-Functional Grammars (LFG). Another important component of this system is the anaphora resolution module. To solve the anaphora, this module contains a method based on linguistic information (lexical, morphological, syntactic and semantic), structural information (anaphoric accessibility space in which the anaphor obtains the antecedent) and statistical information. This method is based on constraints and preferences and solves pronouns and definite descriptions. Moreover, this system fits dialogue and non-dialogue discourse features. The anaphora resolution module uses several resources, such as a lexical database (Spanish WordNet) to provide semantic information and a POS tagger providing the part of speech for each word and its root to make this resolution process easier.

Architecture of a multi-modal dialogue system oriented to multilingual question-answering

In this paper, a proposal of a multi-modal dialogue system oriented to multilingual question-answering is presented. This system includes the following ways of access: voice, text, avatar, gestures and signs language. The proposal is oriented to the question-answering task as a user interaction mechanism. The proposal here presented is in the first stages of its development phase and the architecture is presented for the first time on the base of the experiences in question-answering and dialogues previously developed. The main objective of this research work is the development of a solid platform that will permit the modular integration of the proposed architecture.

Towards the Design of a Textile Chemical Ontology

The main goal of this paper is to present the initial version of a Textile Chemical Ontology, to be used by textile professionals with the purpose of conceptualising and representing the banned and harmful chemical substances that are forbidden in this domain. After analysing different methodologies and determining that “Methontology” is the most appropriate for the purposes, this methodology is explored and applied to the domain. In this manner, an initial set of concepts are defined, together with their hierarchy and the relationships between them. This paper shows the benefits of using the ontology through a real use case in the context of Information Retrieval. The potentiality of the proposed ontology in this preliminary evaluation encourages extending the ontology with a higher number of concepts and relationships, and validating it within other Natural Language Processing applications.

Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications

Recent years have witnessed a surge of interest in computational methods for affect, ranging from opinion mining, to subjectivity detection, to sentiment and emotion analysis. This article presents a brief overview of the latest trends in the field and describes the manner in which the articles contained in the special issue contribute to the advancement of the area. Finally, we comment on the current challenges and envisaged developments of the subjectivity and sentiment analysis fields, as well as their application to other Natural Language Processing tasks and related domains.