M. Teresa Cabré

Automatic specialized vs. non-specialized text differentiation: the usability of grammatical features in a latin multilingual context

In this chapter it is shown that certain grammatical features, besides lexicon, have a strong potential to differentiate specialized texts from non-specialized texts. A tool including these features has been developed and it has been trained using machine learning techniques based on association rules using two sub-corpora (specialized vs. non-specialized), each one divided into training and test corpora. This tool has been evaluated and the results show that the used strategy is suitable to differentiate specialized texts from non-specialized texts. These results could be considered as an innovative perspective to research on domains related with terminology, specialized discourse and computational linguistics, with applications to automatic compilation of Languages for Specific Purposes (LSP) corpora and Adaptive Focused Information Retrieval (AFIR) among others.

Automatic specialized vs. non-specialized sentence differentiation

Compilation of Languages for Specific Purposes (LSP) corpora is a task which is fraught with several difficulties (mainly time and human effort), because it is not easy to discern between specialized and non-specialized text. The aim of this work is to study automatic specialized vs. non-specialized sentence differentiation. The experiments are carried out on two corpora of sentences extracted from specialized and non-specialized texts. One in economics (academic publications and news from newspapers), another about sexuality (academic publications and texts from forums and blogs). First we show the feasibility of the task using a statistical n-gram classifier. Then we show that grammatical features can also be used to classify sentences from the first corpus. For such purpose we use association rule mining.