Jesús Peral

An algorithm for anaphora resolution in Spanish texts

This paper presents an algorithm for identifying noun phrase antecedents of third person personal pronouns, demonstrative pronouns, reflexive pronouns, and omitted pronouns (zero pronouns) in unrestricted Spanish texts. We define a list of constraints and preferences for different types of pronominal expressions, and we document in detail the importance of each kind of knowledge (lexical, morphological, syntactic, and statistical) in anaphora resolution for Spanish. The paper also provides a definition for syntactic conditions on Spanish NP-pronoun noncoreference using partial parsing. The algorithm has been evaluated on a corpus of 1,677 pronouns and achieved a success rate of 76.8. We have also implemented four competitive algorithms and tested their performance in a blind evaluation on the same test corpus. This new approach could easily be extended to other languages such as English, Portuguese, Italian, or Japanese.

A computational approach to zero-pronouns in Spanish

In this paper, a computational approach for resolving zero-pronouns in Spanish texts is proposed. Our approach has been evaluated with partial parsing of the text and the results obtained show that these pronouns can be resolved using similar techniques that those used for pronominal anaphora. Compared to other well-known baselines on pronominal anaphora resolution, the results obtained with our approach have been consistently better than the rest.

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services.

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.

AliQAn, spanish QA system at CLEF-2005

Question Answering is a major research topic at the University of Alicante. For this reason, this year two groups participated in the QA@CLEF track using different approaches. In this paper we describe the work of Alicante 2 group. This paper describes AliQAn, a monolingual open-domain Question Answering (QA) System developed in the Department of Language Processing and Information Systems at the University of Alicante for CLEF-2005 Spanish monolingual QA evaluation task. Our approach is based fundamentally on the use of syntactic pattern recognition in order to identify possible answers. Besides this, Word Sense Disambiguation (WSD) is applied to improve the system. The results achieved (overall accuracy of 33%) are shown and discussed in the paper.

The benefits of the interaction between data warehouses and question answering

Business Intelligence (BI) applications allow their users to query, understand, and analyze existing data within their organizations in order to acquire useful knowledge, thus making better decisions. The core of BI applications is a data warehouse (DW) which integrates several heterogeneous data sources, mainly structured transactional databases. However, a new trend has emerged, since users of BI applications no longer limit their analysis to structured databases, and they also need unstructured sources to empower their analysis (i.e. data from the Web, company-internal reports, emails, etc.). Therefore, the ability to manage this available information is crucial for the success of the decision making process. Unfortunately, this new useful data, although easily available for companies, are often found in unstructured data (such as web pages, internal reports, emails, and so on) which make it unuseful for feeding the DW beneath BI applications. To shed light on this scenario, in this paper, we propose a novel model for using ontologies to integrate Question Answering (QA) systems in the DW. The great benefit of using QA is that specific pieces of data (i.e. answers) that will empower the analysis are detected, obtained and returned from unstructured data in order to feed the DW.Business Intelligence (BI) applications allow their users to query, understand, and analyze existing data within their organizations in order to acquire useful knowledge, thus making better decisions. The core of BI applications is a data warehouse (DW) which integrates several heterogeneous data sources, mainly structured transactional databases. However, a new trend has emerged, since users of BI applications no longer limit their analysis to structured databases, and they also need unstructured sources to empower their analysis (i.e. data from the Web, company-internal reports, emails, etc.). Therefore, the ability to manage this available information is crucial for the success of the decision making process. Unfortunately, this new useful data, although easily available for companies, are often found in unstructured data (such as web pages, internal reports, emails, and so on) which make it unuseful for feeding the DW beneath BI applications. To shed light on this scenario, in this paper, we propose a novel model for using ontologies to integrate Question Answering (QA) systems in the DW. The great benefit of using QA is that specific pieces of data (i.e. answers) that will empower the analysis are detected, obtained and returned from unstructured data in order to feed the DW.

Monolingual and cross-lingual QA using AliQAn and BRILI systems for CLEF 2006

A previous version of AliQAn participated in the CLEF 2005 Spanish Monolingual Question Answering task. For this years run, to the system are added new and representative patterns in question analysis and extraction of the answer. A new ontology of question types has been included. The inexact questions have been improved. The information retrieval engine has been modified considering only the detected keywords from the question analysis module. Besides, many PoS Tagger and SUPAR errors have been solved and finally, dictionaries about cities and countries have been incorporated. To deal with Cross-Lingual tasks, we employ the BRILI system. The achieved results are overall accuracy of 37.89% for monolingual and 21.58% for bilingual tasks.

Application of Data Mining techniques to identify relevant Key Performance Indicators

Currently dashboards are the preferred tool across organizations to monitor business performance. Dashboards are often composed of different data visualization techniques, amongst which are Key Performance Indicators (KPIs) which play a crucial role in quickly providing accurate information by comparing current performance against a target required to fulfill business objectives. However, KPIs are not always well known and sometimes it is difficult to find an appropriate KPI to associate with each business objective. In addition, Data Mining techniques are often used when forecasting trends and visualizing data correlations. In this paper we present a new approach to combining these two aspects in order to drive Data Mining techniques to obtain specific KPIs for business objectives in a semi-automated way. The main benefit of our approach is that organizations do not need to rely on existing KPI lists or test KPIs over a cycle as they can analyze their behavior using existing data. In order to show the applicability of our approach, we apply our proposal to the fields of Massive Open Online Courses (MOOCs) and Open Data extracted from the University of Alicante in order to identify the KPIs. HighlightsExtraction of Key Performance Indicators (KPIs).Application of Data Mining techniques to discover relevant KPIs.A new methodology for extracting the relevant KPIs based on Data mining.Case study with MOOCs and Open Data from the University of Alicante.

Generation of Spanish Zero-Pronouns into English

It is widely agreed that anaphora and ellipsis resolution is an important problem that is still to be solved in Natural Language Processing systems, such as Machine Translation and Information Extraction applications. Zero-pronouns are a special kind of anaphora, whose resolution also lies in ellipsis phenomenon since they do not appear explicitly in the text. They must first be detected (ellipsis), and then resolved just like any other pronoun (anaphora). This kind of pronoun occurs in Spanish texts when they occupy the grammatical position of the subject. In this paper, we propose an approach that resolves zero-pronouns in Spanish texts and subsequently generates them into English. A success rate of 75% has been obtained in the generation of Spanish zero-pronouns into English.

Enrichment of the Phenotypic and Genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Abstract Currently there are an overwhelming number of scientific publications in Life Sciences, especially in Genetics and Biotechnology. This huge amount of information is structured in corporate Data Warehouses (DWs) or in Biological Databases (e.g. UniProt, RCSB Protein Data Bank, CEREALAB or GenBank), whose main drawback is its cost of updating that makes it obsolete easily. However, these Databases are the main tool for enterprises when they want to update their internal information, for example when a plant breeder enterprise needs to enrich its genetic information (internal structured Database) with recently discovered genes related to specific phenotypic traits (external unstructured data) in order to choose the desired parentals for breeding programs. In this paper, we propose to complement the internal information with external data from the Web using Question Answering (QA) techniques. We go a step further by providing a complete framework for integrating unstructured and structured information by combining traditional Databases and DW architectures with QA systems. The great advantage of our framework is that decision makers can compare instantaneously internal data with external data from competitors, thereby allowing taking quick strategic decisions based on richer data.

Coreference-oriented interlingual slot structure & machine translation

One of the main problems of many commercial Machine Translation (MT) and experimental systems is that they do not carry out a correct pronominal anaphora generation. As mentioned in Mitkov (1996), solving the anaphora and extracting the antecedent are key issues in a correct translation. In this paper, we propose an Interlingual mechanism that we have called Interlingual Slot Structure (ISS) based on Slot Structure (SS) presented in Ferrandez et al. (1997). The SS stores the lexical, syntactic, morphologic and semantic information of every constituent of the grammar. The mechanism ISS allows us to translate pronouns between different languages. In this paper, we have proposed and evaluated ISS for the translation between Spanish and English languages. We have compared pronominal anaphora resolution both in English and Spanish to accomplish a study of the existing discrepancies between two languages. This mechanism could be added to a MT system such as an additional module to solve anaphora generation problem.