The Role of Lexical Features in Question Answering for Spanish

Abstract

Abstract. This paper describes the prototype developed in the Language Technologies Laboratory at INAOE for the Spanish monolingual QA evaluation task at CLEF 2005. The proposed approach copes with the QA task according to the type of question to solve (factoid or definition). In order to identify possible answers to factoid questions, the system applies a methodology centered in the use of lexical features. On the other hand, the system is supported by a pattern recognition method in order to identify answers to definition questions. The paper shows the methods applied at different stages of the system, with special emphasis on those used for answering factoid questions. Then the results achieved with this approach are discussed.

Applying Dependency Trees and Term Density for Answer Selection Reinforcement

“…This paper presents the prototype developed as a shared effort between the recently formed eLing Division at VEng and the Language Technologies laboratory at INAOE. This approach continues with the previous work of the authors [9] to cope with factoid questions resolution. The aim of these experiments was to observe and quantify the possible improvement at the answer selection step of a QA prototype, as a consequence of introducing some syntactic features to the decision process.…”

“…As stated before, the system developed for QA@CLEF 2006 is based on the previous work of the authors [9], where the most important modification relies on the inclusion of syntactic features to the decision process at the answer selection module. Figure 1 shows the main blocks of the system.…”

“…Once the system gathers a set of candidate answers, it computes for each candidate answer the lexical weight [9] (see formula 1) in order to rank the best one and then selects the top-n as the system answers. However, there were situations where several candidate answers could have the same weight.…”

Classifier combination approach for question classification for Bengali question answering system

“…Presently, QA systems developed for European [32][33][34], Middle Eastern [35][36][37] and Asian languages [38][39][40][41] are capable of providing answers with reasonable accuracy. However, the scenario is different for Indian languages, in which QA research is in a nascent stage.…”

Open IE as an Intermediate Structure for Semantic Tasks

“…Common intermediate structures include: (1) Lexical representations, in which features are extracted from the original word sequence or the bag of words, (2) Stanford dependency parse trees (De Marneffe and Manning, 2008), which draw syntactic relations between words, and (3) Semantic role labeling (SRL), which extracts frames linking predicates with their semantic arguments (Carreras and Màrquez, 2005). For instance, a QA application can evaluate a question and a candidate answer by examining their lexical overlap (Pérez-Coutiño et al, 2006), by using short dependency paths as features to compare their syntactic relationships (Liang et al, 2013), or by using SRL to compare their predicate-argument structures (Shen and Lapata, 2007).…”