Sparsity is one of the major problems in natural language processing. The problem becomes even more severe in agglutinating languages that are highly prone to be inflected. We deal with sparsity in Turkish by adopting morphological features for part-of-speech tagging. We learn inflectional and derivational morpheme tags in Turkish by using conditional random fields (CRF) and we employ the morpheme tags in part-of-speech (PoS) tagging by using hidden Markov models (HMMs) to mitigate sparsity. Results show that using morpheme tags in PoS tagging helps alleviate the sparsity in emission probabilities. Our model outperforms other hidden Markov model based PoS tagging models for small training datasets in Turkish. We obtain an accuracy of 94.1 in morpheme tagging and 89.2 in PoS tagging on a 5K training dataset.
Morpheme tags have been used in many natural language processing tasks. El-Kahlout and Oflazer @cite_8 employ morphological tags in order to alleviate the sparsity by matching the Turkish morphemes having the same morphological tag to the same English translation in statistical machine translation task. They address that using morphological tags provides a substantial improvement on the BLUE score.
@cite_8
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We present statistical models for morphological disambiguation in agglutinative languages, with a specific application to Turkish. Turkish presents an interesting problem for statistical models as the potential tag set size is very large because of the productive derivational morphology. We propose to handle this by breaking up the morphosyntactic tags into inflectional groups, each of which contains the inflectional features for each (intermediate) derived form. Our statistical models score the probability of each morphosyntactic tag by considering statistics over the individual inflectional groups and surface roots in trigram models. Among the four models that we have developed and tested, the simplest model ignoring the local morphotactics within words performs the best. Our best trigram model performs with 93.95 accuracy on our test data getting all the morhosyntactic and semantic features correct. If we are just interested in syntactically relevant features and ignore a very small set of semantic features, then the accuracy increases to 95.07 . This paper describes the application of the perceptron algorithm to the morphological disambiguation of Turkish text. Turkish has a productive derivational morphology. Due to the ambiguity caused by complex morphology, a word may have multiple morphological parses, each with a different stem or sequence of morphemes. The methodology employed is based on ranking with perceptron algorithm which has been successful in some NLP tasks in English. We use a baseline statistical trigram-based model of a previous work to enumerate an n-best list of candidate morphological parse sequences for each sentence. We then apply the perceptron algorithm to rerank the n-best list using a set of 23 features. The perceptron trained to do morphological disambiguation improves the accuracy of the baseline model from 93.61 to 96.80 . When we train the perceptron as a POS tagger, the accuracy is 98.27 . Turkish morphological disambiguation and POS tagging results that we obtained is the best reported so far. This paper presents some very preliminary results for and problems in developing a statistical machine translation system from English to Turkish. Starting with a baseline word model trained from about 20K aligned sentences, we explore various ways of exploiting morphological structure to improve upon the baseline system. As Turkish is a language with complex agglutinative word structures, we experiment with morphologically segmented and disambiguated versions of the parallel texts in order to also uncover relations between morphemes and function words in one language with morphemes and functions words in the other, in addition to relations between open class content words. Morphological segmentation on the Turkish side also conflates the statistics from allomorphs so that sparseness can be alleviated to a certain extent. We find that this approach coupled with a simple grouping of most frequent morphemes and function words on both sides improve the BLEU score from the baseline of 0.0752 to 0.0913 with the small training data. We close with a discussion on why one should not expect distortion parameters to model word-local morpheme ordering and that a new approach to handling complex morphotactics is needed. This paper presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods. In languages like English, there is a very small number of possible word forms with a given root word. However, languages like Turkish have very productive agglutinative morphology. Thus, it is an issue to build statistical models for specific tasks using the surface forms of the words, mainly because of the data sparseness problem. In order to alleviate this problem, we used additional syntactic information, i.e. the morphological structure of the words. We have successfully applied statistical methods using both the lexical and morphological information to sentence segmentation, topic segmentation, and name tagging tasks. For sentence segmentation, we have modeled the final inflectional groups of the words and combined it with the lexical model, and decreased the error rate to 4.34 , which is 21 better than the result obtained using only the surface forms of the words. For topic segmentation, stems of the words (especially nouns) have been found to be more effective than using the surface forms of the words and we have achieved 10.90 segmentation error rate on our test set according to the weighted TDT-2 segmentation cost metric. This is 32 better than the word-based baseline model. For name tagging, we used four different information sources to model names. Our first information source is based on the surface forms of the words. Then we combined the contextual cues with the lexical model, and obtained some improvement. After this, we modeled the morphological analyses of the words, and finally we modeled the tag sequence, and reached an F-Measure of 91.56 , according to the MUC evaluation criteria. Our results are important in the sense that, using linguistic information, i.e. morphological analyses of the words, and a corpus large enough to train a statistical model significantly improves these basic information extraction tasks for Turkish.