POS tagging in Amazigh using support vector machines and conditional random fields

The aim of this paper is to present the first Amazighe POS tagger. Very few linguistic resources have been developed so far for Amazighe and we believe that the development of a POS tagger tool is the first step needed for automatic text processing. The used data have been manually collected and annotated. We have used state-of-art supervised machine learning approaches to build our POS-tagging models. The obtained accuracy achieved 92.58% and we have used the 10-fold technique to further validate our results. © Springer-Verlag Berlin Heidelberg 2011We would like to thank all IRCAM researchers for their valuable assistance. The work of the third author was funded by the MICINN research project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i).Outahajala, M.; Benajiba, Y.; Rosso, P.; Zenkouar, L. (2011). POS tagging in Amazigh using support vector machines and conditional random fields. En Natural Language Processing and Information Systems. Springer Verlag (Germany). 6716:238-241. https://doi.org/10.1007/978-3-642-22327-3_28S238241671

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

L'étiquetage grammatical de l'amazighe en utilisant les propriétés n-grammes et un prétraitement de segmentation

[FR] L’objectif de cet article est de présenter le premier étiqueteur grammatical amazighe. Très peu de ressources ont été développées pour l’amazighe et nous croyons que le développement d’un outil d’étiquetage grammatical est une étape préalable au traitement automatique de textes. Afin d'atteindre cet objectif, nous avons formé deux modèles de classification de séquences en utilisant les SVMs, séparateurs à vaste marge (Support Vector Machines) et les CRFs, champs markoviens conditionnels (Conditional Random Fields) en utilisant une phase de segmentation. Nous avons utilisé la technique de 10 fois la validation croisée pour évaluer notre approche. Les résultats montrent que les performances des SVMs et des CRFs sont très comparables. Dans l'ensemble, les SVMs ont légèrement dépassé les CRFs au niveau des échantillons (92,58% contre 92,14%) et la moyenne de précision des CRFs dépasse celle des SVMs (89,48% contre 89,29%). Ces résultats sont très prometteurs étant donné que nous avons utilisé un corpus de seulement ~ 20k mots.[EN] The aim of this paper is to present the first amazigh POS tagger. Very few linguistic resources have been developed so far for amazigh and we believe that the development of a POS tagger tool is the first step needed for automatic text processing. In order to achieve this endeavor, we have trained two sequence classification models using Support Vector Machines (SVMs) and Conditional Random Fields (CRFs) after using a tokenization step. We have used the 10- fold technique to evaluate our approach. Results show that the performance of SVMs and CRFs are very comparable. Across the board, SVMs outperformed CRFs on the fold level (92.58% vs. 92.14%) and CRFs outperformed SVMs on the 10 folds average level (89.48% vs. 89.29%). These results are very promising considering that we have used a corpus of only ~20k tokens.Les travaux du troisième auteur ont été financés par le projet de recherche EU FP7 Marie Curie PEOPLE-IRSES 269180 WiQ-Ei, MICINN TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i), VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Outahajala, M.; Benajiba, Y.; Rosso, P.; Zenkouar, L. (2012). L'étiquetage grammatical de l'amazighe en utilisant les propriétés n-grammes et un prétraitement de segmentation. E-TI : la revue électronique des technologies de l'information. 6:48-61. http://hdl.handle.net/10251/47570S4861

Creating language resources for under-resourced languages: methodologies, and experiments with Arabic

Language resources are important for those working on computational methods to analyse and study languages. These resources are needed to help advancing the research in fields such as natural language processing, machine learning, information retrieval and text analysis in general. We describe the creation of useful resources for languages that currently lack them, taking resources for Arabic summarisation as a case study. We illustrate three different paradigms for creating language resources, namely: (1) using crowdsourcing to produce a small resource rapidly and relatively cheaply; (2) translating an existing gold-standard dataset, which is relatively easy but potentially of lower quality; and (3) using manual effort with appropriately skilled human participants to create a resource that is more expensive but of high quality. The last of these was used as a test collection for TAC-2011. An evaluation of the resources is also presented