In this article we present the application of transformation-based learning (TBL) [1] to the task of assigning tags to postings in online chat conversations. We define a list of posting tags that have proven useful in chat-conversation analysis. We describe the templates used for posting act tagging in the context of template selection. We extend traditional approaches used in part-of-speech ta...

While large POS(part-of-speech) annotated corpora play an important role in natural language processing, the annotated corpus requires very high accuracy and consistency. To build such an accurate and consistent corpus, we often use a manual tagging method. But the manual tagging is very labor intensive and expensive. Furthernaore, it is not easy to get consistent results from the humari expert...

Ensuring consistency of Part-Of-Speech (POS) tagging plays an important role in the construction of high-quality Chinese corpora. After having analyzed the POS tagging of multi-category words in large-scale corpora, we propose a novel classification-based consistency checking method of POS tagging in this paper. Our method builds a vector model of the context of multi-category words along with ...

Ensuring consistency of Part-of-Speech (POS) tagging plays an important role in constructing high-quality Chinese corpora. After analyzing the POS tagging of multi-category words in largescale corpora, we propose a novel consistency check method of POS tagging in this paper. Our method builds a vector model of the context of multicategory words, and uses the k-NN algorithm to classify context v...

We present an approach to using a morphological analyzer for tokenizing and morphologically tagging (including partof-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We obtain accuracy rates on all tasks in the

While probabilistic methods of part-of-speech tag assignment have long received consideration in corpus and computational-linguistic research, less attention would appear to have been paid to date to the development of tagging accuracy over rounds of iterative, interactive training in applications of these methods. Understanding this aspect of probabilistic tagging is arguably of particular imp...

In this paper we propose an approach to Part of Speech (PoS) tagging using a combination of Hidden Markov Model and error driven learning. For the NLPAI joint task, we also implement a chunker using Conditional Random Fields (CRFs). The results for the PoS tagging and chunking task are separately reported along with the results of the joint task.

The paper is devoted to the issue of correction of the erroneous and ambiguous corpus of Frequency Dictionary of Contemporary Polish (FDCP) and its application to morphosyntactic tagging of the Polish language. Several stages of corpus transformation are presented and baseline part-of-speech tagging algorithms are evaluated, too.

A Bayesian method for incorporating probabilistic background knowledge into ILP is presented. Positive only learning is extended to allow density estimation. Estimated densities and deened prior are combined in Bayes theorem to perform relational classiication. An initial application of the technique is made to part-of-speech (POS) tagging. A novel use of Gibbs sampling for POS tagging is given.

Since most previous works for HMM-based tagging consider only part-of-speech information in contexts, their models cannot utilize lexical information which is crucial for resolving some morphological ambiguity. In this paper we introduce uniformly lexicalized HMMs for partof-speech tagging in both English and Korean. The lexicalized models use a simpli ed back-o smoothing technique to overcome ...