Event ordering through temporal expression resolution

In this paper a multilingual method for event ordering based on temporal expression resolution is presented. This method has been implemented through the TERSEO system which consists of three main units: temporal expression recognizing, resolution of the coreference introduced by these expressions, and event ordering. By means of this system, chronological information related to events can be extracted from documental databases. This information is automatically added to the documental database in order to allow its use by question answering systems in those cases referring to temporality. The system has been evaluated obtaining results of 91 % precision and 71 % recall. For this, a blind evaluation process has been developed guaranteing a reliable annotation process that was measured through the kappa factor.This paper has been supported by the Spanish government, projects FIT-150500-2002-244 and FIT-150500-2002-416

Paralelismo sintáctico-semántico para el tratamiento de elementos extrapuestos en textos no restringidos

En este artículo presentamos un método basado en la teoría del paralelismo para la identificación y resolución de elementos extrapuestos en textos no restringidos. Esta teoría de paralelismo está basada en (Palomar 96) y se amplía con el desarrollo de técnicas de análisis parcial –en las que se estudia las partes relevantes del texto- que facilitan la resolución de los fenómenos lingüísticos. Nos basaremos en los programas Datalog extendidos (Dahl 94) (Dahl 95) como herramienta para la definición e implementación de gramáticas. Éstas no están basadas en reglas gramaticales sino en la detección de información relevante, relajando el proceso y ampliando el conjunto potencial de textos analizables.Este artículo ha sido subvencionado por el proyecto CICYT nº TIC97-0671-C02-01/02

EmotiBlog: towards a finer-grained sentiment analysis and its application to opinion mining

Comunicación presentada en las IV Jornadas TIMM, Torres (Jaén), 7-8 abril 2011.EmotiBlog is a corpus designed for Sentiment Analysis research. Preliminary studies demonstrated its relevance as a Machine Learning resource for detecting subjective information. In this paper we explore additional features by a detailed analysis. In addition, we compare EmotiBlog with other well-known Sentiment Analysis resource such as the JRC corpus. Finally, as a result of our research, we developed an Opinion Mining application, which takes into account user opinions when rating the results of a search engine specialized in mobile phones

Social Rankings: Visual Sentiment Analysis in Social Networks

Social Rankings es una aplicación web que realiza un seguimiento en tiempo real de entidades en las redes sociales. Detecta y analiza las opiniones sobre estas entidades utilizando técnicas de análisis de sentimientos para generar un informe visual de su valoración y su evolución en el tiempo.Social Rankings is a web application that follows different entities in the social networks in real time. It detects and analyses the opinions about these entities using sentiment analysis techniques, to generate a visual report of their reputation and evolution in time.Social Rankings ha sido desarrollada por el Grupo de Procesamiento del Lenguaje Natural y Sistemas de Información (GPLSI) de la Universidad de Alicante. Esta aplicación ha sido financiada parcialmente por el Gobierno Español a través de los proyectos ATTOS (TIN2012-38536-C03-03) y LEGOLANG (TIN2012-31224), la Comisión Europea a través del proyecto SAM (FP7-611312), la Generalitat Valenciana a través del proyecto DIIM2.0 (PROMETEOII/2014/001) y la Universidad de Alicante a través del proyecto emergente “Explotación y tratamiento de la información disponible en Internet para la anotación y generación de textos adaptados al usuario” (GRE13-15)

Annotated Corpus for Citation Context Analysis

In this paper, we present a corpus composed of 85 scientific articles annotated with 2092 citations analyzed using context analysis. We obtained a high Inter-annotator agreement; therefore, we assure reliability and reproducibility of the annotation performed by three coders in an independent way. We applied this corpus to classify citations according to qualitative criteria using a medium granularity categorization scheme enriched by annotated keywords and labels to obtain high granularity. The annotation schema handle three dimensions: PURPOSE: POLARITY: ASPECTS. Citation purpose define functions classification: use, critique, comparison and background with more specific classes stablished using keywords: Based on, Supply; Useful; Contrast; Acknowledge, Corroboration, Debate; Weakness and Hedges. Citation aspects complement the citation characterization: concept, method, data, tool, task, among others. Polarity has three levels: Positive, Negative and Neutral. We developed the schema and annotated the corpus focusing in applications for citation influence assessment, but we suggest that applications as summary generation and information retrieval also could use this annotated corpus because of the organization of the scheme in clearly defined general dimensions

Analysing Opinions in Social Networks

La Web 2.0 ha focalizado la importancia de la información, no en unos pocos expertos en un tema, sino en una multitud de opiniones vertidas por usuarios a través de diversos medios en las redes sociales. Debido a ello, han cobrado un mayor interés los sistemas que son capaces de determinar qué es lo que piensan los usuarios sobre un determinado concepto, agregando diferentes fuentes de datos y aplicando cálculos de polaridad de las opiniones, que permiten determinar y comparar esos conceptos con otros similares. En este artículo describimos Social Analytics, nuestra visión sobre cómo deberían funcionar este tipo de sistemas, con una interfaz simple y optimizada que permita responder las necesidades de los usuarios.Web 2.0 has focused the importance of information, not on a few experts on a topic, but on a multitude of opinions expressed by users through various media on social networks. Due to this, there has been a increasing interest in systems that are able to determine what users think about a certain concept, by adding different sources of data and applying polarity calculations, to determine and compare these concepts with similar ones. In this paper we describe Social Analytics, our vision on how these kind of system should work, with a simple and optimized interface to meet the needs of the users.Este trabajo ha sido parcialmente financiado por el Ministerio de Educación, Cultura y Deporte (MECD FPU014/00983), y la Universidad de Alicante, la Generalitat Valenciana y el Gobierno Español a través de los proyectos TIN2015-65136-C2-2- R, TIN2015-65100-R, PROMETEOII/2014/001 y FUNDACIONBBVA2-16PREMIOI

Going beyond traditional QA systems: challenges and keys in opinion question answering

The treatment of factual data has been widely studied in different areas of Natural Language Processing (NLP). However, processing subjective information still poses important challenges. This paper presents research aimed at assessing techniques that have been suggested as appropriate in the context of subjective - Opinion Question Answering (OQA). We evaluate the performance of an OQA with these new components and propose methods to optimally tackle the issues encountered. We assess the impact of including additional resources and processes with the purpose of improving the system performance on two distinct blog datasets. The improvements obtained for the different combination of tools are statistically significant. We thus conclude that the proposed approach is adequate for the OQA task, offering a good strategy to deal with opinionated questions.This paper has been partially supported by Ministerio de Ciencia e Innovación - Spanish Government (grant no. TIN2009-13391-C04-01), and Conselleria d'Educación - Generalitat Valenciana (grant no. PROMETEO/2009/119 and ACOMP/2010/286)

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EmoLabel: Semi-Automatic Methodology for Emotion Annotation of Social Media Text

The exponential growth of the amount of subjective information on the Web 2.0. has caused an increasing interest from researchers willing to develop methods to extract emotion data from these new sources. One of the most important challenges in textual emotion detection is the gathering of data with emotion labels because of the subjectivity of assigning these labels. Basing on this rationale, the main objective of our research is to contribute to the resolution of this important challenge. This is tackled by proposing EmoLabel: a semi-automatic methodology based on pre-annotation, which consists of two main phases: (1) an automatic process to pre-annotate the unlabelled English sentences; and (2) a manual process of refinement where human annotators determine which is the dominant emotion. Our objective is to assess the influence of this automatic pre-annotation method on manual emotion annotation from two points of view: agreement and time needed for annotation. The evaluation performed demonstrates the benefits of pre-annotation processes since the results on annotation time show a gain of near 20% when the pre-annotation process is applied (Pre-ML) without reducing annotator performance. Moreover, the benefits of pre-annotation are higher in those contributors whose performance is low (inaccurate annotators).This research has been supported by the Spanish Government (ref. RTI2018-094653-B-C22) and the Valencian Government (grant no. PROMETEU/2018/089). It has also been funded by the FPI grant (BES-2013-065950) and the research stay grant (EEBB-I-17-12578) from the Spanish Ministry of Science and Innovation