Understanding How Headings Influence Text Processing

Boosting for Text Classification with Subject Headings

s: The aim of this study is to investigate how Medical Subject Headings (MeSH) as background knowledge source can improve text classification results. The hypothesis is experimented with two different sets of medical documents using HMM-based TC classifier. Experimental results show the improvement of the performance with MeSH in accuracy. Résumé : Le but de cette étude est d’examiner comment l...

Text structures in medical text processing: empirical evidence and a text understanding prototype

We consider the role of textual structures in medical texts. In particular, we examine the impact the lacking recognition of text phenomena has on the validity of medical knowledge bases fed by a natural language understanding front-end. First, we review the results from an empirical study on a sample of medical texts considering, in various forms of local coherence phenomena (anaphora and text...

Natural Language Understanding in Façade: Surface-Text Processing

Understanding Text Pre-Processing for Latent Dirichlet Allocation

To apply natural language modeling techniques to new corpora requires users to convert documents to data using various pre-processing treatments. However, the effects of these transformations are still poorly understood. We describe several studies that quantify the impact of preprocessing in different forms, focusing on topic modeling applications. We find that many common practices either hav...

The Influence of Text Pre-processing on Plagiarism Detection

This paper explores the influence of text preprocessing techniques on plagiarism detection. We examine stop-word removal, lemmatization, number replacement, synonymy recognition, and word generalization. We also look into the influence of punctuation and word-order within N-grams. All these techniques are evaluated according to their impact on F1-measure and speed of execution. Our experiments ...