Learning-Free Text Categorization

Abstract

Abstract. In this paper, we report on the fusion of simple retrieval strategies with thesaural resources in order to perform large-scale text categorization tasks. Unlike most related systems, which rely on training data in order to infer text-to-concept relationships, our approach can be applied with any controlled vocabulary and does not use any training data. The first classification module uses a traditional vector-space retrieval engine, which has been fine-tuned for the task, while the second classifier is based on regular variations of the concept list. For evaluation purposes, the system uses a sample of MedLine and the Medical Subject Headings (MeSH) terminology as collection of concepts. Preliminary results show that performances of the hybrid system are significantly improved as compared to each single system. For top returned concepts, the system reaches performances comparable to machine learning systems, while genericity and scalability issues are clearly in favor of the learningfree approach. We draw conclusion on the importance of hybrids strategies combining data-poor classifiers and knowledge-based terminological resources for general text mapping tasks.

A recent advance in the automatic indexing of the biomedical literature

“…Although the automatic assignment of MeSH indexing terms to a body of biomedical text has been extensively studied in the literature (see for example [1], [5], [6], [7], [8], [9], [10]), several major aspects of the task are often misunderstood or understated. Most issues pertain to the following topics:

multi-label assignment

scalability

compliance with indexing policies

…”

“…Note that in [6], MeSH main headings are referred to as “MeSH categories”. Most efforts addressing MeSH indexing attempt to tackle indexing by solely using main headings which involves about 24,000 categories [1], [7]. However, in practice, MeSH indexing terms also include main heading/subheading pairs.…”

Automatic medical encoding with SNOMED categories

“…For a short introduction on automatic text categorization in MEDLINE, the reader is referred to the NLM's indexing initiative [ 9 ]; for a detailed presentation of our vector space engine and a comparison with state-of-the-art systems, including NLM's tools, see [ 3 ](in this joint evaluation between four retrieval systems, our engine showed competitive performances) [ 10 ]. For a complete overview and evaluation of our categorization system applied on Medical Subject Headings and on the Gene Ontology, see [ 11 ].…”

Indexation automatique de documents en santé : évaluation et analyse de sources d’erreurs

“…Il faut signaler que l'équipe CISMeF a réalisé en 2007 une évaluation de l'indexation automatique [26] [29] et NomIndex [14], cela en utilisant un corpus en français « misc » et les ressources en français du corpus « ENFR » [30]. Pour un rang égal à 10 (ce rang représente le nombre de mots clés classés selon un score calculé), les valeurs de rappel Cette comparaison entre les méthodes d'indexation automatique reste approximative, car les corpus de test sont différents et indexés manuellement par des indexeurs différents.…”