Sentence examples for term weighting from inspiring English sources

We incorporate the common textual term weighting schemes in the BOVW model.

In the next step, each text segment is represented in vector space model as a feature vector with term weighting.

This allows term weighting concepts used for content-based retrieval, such as term frequency and inverse document frequency, to translate directly to concepts for structure-based retrieval.

We theoretically show that the new representation scheme subsumes the traditional Vector Space model representation scheme as a special case and hence allows for additional flexibility in term weighting.

In this study, we propose a new feature selection method, called query expansion ranking, which is based on query expansion term weighting methods from the field of information retrieval.

In comparison with equal term weighting, the performance was significantly improved by specific term weighting (p < 0.01) but not by generic term weighting.

In comparison to equal term weighting, the performance was improved by specific term weighting approach but worsened by generic term weighting approach.

Additional file 4: Similarity Scores with Specific Term Weighting.

Additional file 3: Similarity Scores with Generic Term Weighting.

Normalizing for document lengths in the term weighting method usually compensates this effect.

Here we propose a term weighting approach supported by PubMed search engine to address this issue.