Experimental results indicate that our approach of identifying question topic and question focus for search significantly outperforms the baseline methods such as Vector Space Model (VSM) and Language Model for Information Retrieval (LMIR).

To obtain the ground-truth of question search, we employed the Vector Space Model (VSM) (Salton et al., 1975) to retrieve the top 20 results and obtained manual judgments.

vector space model, Okapi, language model, and translation-based model, within the setting of question search (Jeon et al., 2005b).

Conventional vector space models are used to calculate the statistical similarity and WordNet (Fellbaum, 1998) is used to estimate the semantic similarity.

vector space model, Okapi, language model (LM), and translation-based model, for automatically fixing the lexical chasm between

As regards the nature of this task, a vector space model (VSM) is a straightforward and suitable representation for statistical learning.

Our experiments demonstrated that it is indeed a good idea to include longer phrases in the vector space model representation of sentences.

Since hedge cues cause systems to predict false positive labels, our idea here was to train Maximum Entropy Models for the false positive classifications of our ICD-9-CM coding system using the vector space representation of radiology reports.