Toward a Document Model for Question Answering Systems

Abstract

Abstract. The problem of acquiring valuable information from the large amounts available today in electronic media requires automated mechanisms more natural and efficient than those already existing. The trend in the evolution of information retrieval systems goes toward systems capable of answering specific questions formulated by the user in her/his language. The expected answers from such systems are short and accurate sentences, instead of large document lists. On the other hand, the state of the art of these systems is focused -mainly-in the resolution of factual questions, whose answers are named entities (dates, quantities, proper nouns, etc). This paper proposes a model to represent source documents that are then used by question answering systems. The model is based on a representation of a document as a set of named entities (NEs) and their local lexical context. These NEs are extracted and classified automatically by an off-line process. The entities are then taken as instance concepts in an upper ontology and stored as a set of DAML+OIL resources which could be used later by question answering engines. The paper presents a case of study with a news collection in Spanish and some preliminary results.

The Role of Lexical Features in Question Answering for Spanish

“…Tagged passages are represented in the same way as proposed in [4] where each retrieved passage is modeled by the system as a factual text object whose content refers to several named entities 2 even when it could be focused on a central topic. The model assumes that the named entities are strongly related to their lexical context, especially to nouns (subjects) and verbs (actions).…”

Applying Dependency Trees and Term Density for Answer Selection Reinforcement

“…The second part of the process requires the POS and Parsing tagged forms of each passage in order to gather the representation used to extract candidates answers. Tagged passages are represented as described in [8] where each retrieved passage is modeled by the system as a factual text object whose content refers to several named entities even when it is focused on a central topic. The model assumes that the named entities are strongly related to their lexical context, especially to nouns (subjects) and verbs (actions).…”

Question Answering for Spanish Based on Lexical and Context Annotation

“…Thus, a document can be seen as a set of entities and their contexts. For details about the document model we refer the reader to [7]. In order to obtain the representation of the documents, the system begins preprocessing each document with MACO, where this process is performed off-line.…”

“…Such approach lies in the concept of redundancy in the web, i.e, the module applies a several transformations in order to convert the question into a typical query and then this query along to some query reformulations are sent to a search engine with the hypothesis that the answer would be contained -several times-in the snippets retrieved by the search engine 7 . The selection of candidate answers from Internet is based on computing all the n-grams, from unigrams to pentagrams, as possible answers to the given question.…”