Chunking in Turkish with Conditional Random Fields

In this paper, we report our work on chunking in Turkish. We used the data that we generated by manually translating a subset of the Penn Treebank. We exploited the already available tags in the trees to automatically identify and label chunks in their Turkish translations. We used conditional random fields (CRF) to train a model over the annotated data. We report our results on different levels of chunk resolution.Publisher's Versio

Evaluation of a Grammar of French Determiners

Existing syntactic grammars of natural languages, even with a far from complete coverage, are complex objects. Assessments of the quality of parts of such grammars are useful for the validation of their construction. We evaluated the quality of a grammar of French determiners that takes the form of a recursive transition network. The result of the application of this local grammar gives deeper syntactic information than chunking or information available in treebanks. We performed the evaluation by comparison with a corpus independently annotated with information on determiners. We obtained 86% precision and 92% recall on text not tagged for parts of speech.Comment: 10 page

Indonesian Named-entity Recognition for 15 Classes Using Ensemble Supervised Learning

AbstractHere, we describe our effort in building Indonesian Named Entity Recognition (NER) for newspaper article with 15 classes which is larger number of class type compared to existing Indonesian NER. We employed supervised machine learning in the NER and conducted experiments to find the best attribute combination and the best algorithm with highest accuracy. We compared the attribute of word level, sentence level and document level. In the algorithm, we compared several single machine learning algorithms and also an ensembled one. Using 457 news articles, the best accuracy was achieved by using ensemble technique where the result of several machine learning algorithms were used as the feature for one machine learning algorithm