Aiala Rosá

Combining Rules and CRF Learning for Opinion Source Identification in Spanish Texts

In this work we present a system for the automatic annotation of opinions in Spanish texts. We focus mainly in the definition of a TFS-style model for the predicates of opinion and their arguments, in the creation of a lexicon of opinion predicates and in two additional variants for identifying the source of opinions. The original system extracts opinions and all its elements (predicate, source, topic and message) based on hand-coded rules, the first variant uses a CRF model for learning the source, assuming that the predicate is already tagged, and the second variant is a combined version, with the result of source recognition via the rule-based system being added as an additional attribute for training the CRF model. We found that this hybrid system performs better than each of the systems evaluated separately. This work involved the construction of several resources for Spanish: a lexicon of opinion predicates, a 13,000 word corpus with whole opinion annotations and a 40,000 word corpus with annotations of opinion predicates and sources.

Coreference resolution between sources of opinions in Spanish texts

This paper presents a system that identifies coreferent elements in Spanish texts, with the purpose of contributing to an opinion extraction project. In particular, the system looks for the actual source when it is not identified in the opinion and solves coreferences between opinion sources in digital media texts in Spanish. The developed system takes as input texts where the opinions have already been identified, and uses syntactic and semantic information to identify the relationships between the different entities, in order to create coreference chains between the sources of the opinions in the text. The algorithm uses a score method to select the correct antecedent between the candidates. It achieved a precision of 82.8% and a recall of 85.6%.

Opinion Search in Spanish Written Press

We describe a press reading tool that focuses on sayings or opinions in the news about topics and sources of the reader’s choice. This tool offers a graphical interface through which several sources can be linked given a certain topic and which allows readers to visualize opinions in a timeline and browse through them. This is done over a corpus that includes three Uruguayan written press media and integrates their current and previous editions. The opinion recognizer is mainly rule-based, with rules written in a formalism called Contextual Rules and machine learning methods and regular expressions rules were used during the different stages of the system. The accuracy of the system was evaluated with respect to the information retrieved, showing 76% precision.