Towards an Enhanced Vector Model to Encode Textual Relations: Experiments Retrieving Information

The constant growth of digital information, facilitated by storage technologies, imposes new challenges for information processing tasks, and maintains the need of effective search mechanisms, oriented towards improving in precision but simultaneously capable of producing useful information in a short time. Hence, this paper presents a document representation to encode textual relations. This representation does not consider each term as one entry in a vector but rather as a pattern, i.e. a set of contiguous entries. To deal with variations inherent in natural language, we plan to express textual relations (such as noun phrases, named entities, subject-verb, verb-object, adjective-noun, and adverb-verb) as composed patterns. An operator is applied to form bindings between terms encoding relations as new “terms”, thereby providing additional descriptive elements for indexing a document collection. The results of our first experiments, using the document representation to conduct information retrieval and incorporating two-word noun phrases, showed that the representation is feasible, retrieves, and improves the ranking of relevant documents, and consequently the values of mean average precision.