Language modeling approaches have been effectively dealing with the dependency among query terms based on N-gram such as bigram or trigram models. However, bigram language models suffer from adjacency-sparseness problem which means that dependent terms are not always adjacent in documents, but can be far from each other, sometimes with distance of a few sentences in a document. To resolve the adjacency-sparseness problem, this paper proposes a new type of bigram language model by explicitly incorporating the proximity feature between two adjacent terms in a query. Experimental results on three test collections show that the proposed bigram language model significantly improves previous bigram model as well as Tao's approach, the state-of-art method for proximity-based method.

In natural language database interfaces (NLDBI), manual construction of translation knowledge normally undermines domain portability because of its expensive human intervention. To overcome this, this paper proposes linguistically-motivated database semantics in order to systematically construct translation knowledge. Database semantics consists of two structures which are designed to function as a translation dictionary, and to contain selectional restriction constraints on domain classes. The database semantics is semi-automatically obtained from a semantic data model for a target database. Based on this database semantics, a conceptual NLDBI translation scheme is developed. Translating a natural language question into a database query suffers from the translation ambiguity problem, which has not received significant attention in this field. To deal with translation ambiguity, we suggest an ambiguity resolution method based on the proposed database semantics. Experiments showed the usefulness of the proposed database semantics and its disambiguation procedures.

In a dictionary-based query translation for cross-language text retrieval, transfer ambiguity is one of the main causes of performance deterioration, but the problem has not received significant attention in this field. To resolve transfer ambiguity, this paper proposes a two-phase query translation based on term reweighting, which uses a bilingual transfer dictionary, originally designed for machine translation. In general, source language query terms each show some word association with others, so that their correct translations should be more likely to co-occur in target documents. Based on this simple intuition, the first phase discriminates more relevant target documents from the others. Using statistical and ranking information from the highly relevant documents, the second phase then converts a translated query vector into reweighted form to add an extra weight on probably correct target terms. In experiments, the results were remarkable: the proposed method achieved almost the same performance as the monolingual IR system, actually contributing to an improvement of precision by about 9% over a baseline system.