Part of Speech Taggers for Morphologically Rich Indian Languages: A Survey <s> HMM & ME Based Tagging <s> Abstract : Contents: Natural Language Processing Overview; Mechanical Translation, Grammars--Formal Grammars; Transformational Grammars; Systemic Grammar; Case Grammar; Parsing--Overview of Parsing Techniques; Augmented Transition Nets; The General Syntactic Processor; Text Generation; Natural Language Processing Systems--Early Natural Language Systems; Wilk's Mechanical Translation System; LUNAR, SHRDLU, MARGIE, SAM and PAM, LIFER. <s> BIB001 </s> Part of Speech Taggers for Morphologically Rich Indian Languages: A Survey <s> HMM & ME Based Tagging <s> Five language and tagset independent stochastic taggers, handling morphological and contextual information, are presented and tested in corpora of seven European languages (Dutch, English, French, German, Greek, Italian and Spanish), using two sets of grammatical tags; a small set containing the eleven main grammatical classes and a large set of grammatical categories common to all languages. The unknown words are tagged using an experimentally proven stochastic hypothesis that links the stochastic behavior of the unknown words with that of the less probable known words. A fully automatic training and tagging program has been implemented on an IBM PC-compatible 80386-based computer. Measurements of error rate, time response, and memory requirements have shown that the taggers' performance is satisfactory, even though a small training text is available. The error rate is improved when new texts are used to update the stochastic model parameters. <s> BIB002 </s> Part of Speech Taggers for Morphologically Rich Indian Languages: A Survey <s> HMM & ME Based Tagging <s> Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora. <s> BIB003 </s> Part of Speech Taggers for Morphologically Rich Indian Languages: A Survey <s> HMM & ME Based Tagging <s> This paper describes our work on building Part-of-Speech (POS) tagger for Bengali. We have use Hidden Markov Model (HMM) and Maximum Entropy (ME) based stochastic taggers. Bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. Since only a small labeled training set is available (45,000 words), simple stochastic approach does not yield very good results. In this work, we have studied the effect of using a morphological analyzer to improve the performance of the tagger. We find that the use of morphology helps improve the accuracy of the tagger especially when less amount of tagged corpora are available. <s> BIB004
Stochastic models (Cutting et al.,  ; Dermatas et al., BIB002 ; Brants, BIB003 ) have been widely used in POS tagging for simplicity and language independence of the models. Among stochastic models, bi-gram and tri-gram Hidden Markov Model (HMM) are quite popular. In this work supervised and semi-supervised bi-gram HMM & a ME based model was explored. The tagset used consists of 40 tags. The bi-gram assumption states that the POStag of a word depends on the current word and the POS tag of the previous word. An ME model estimates the probabilities based on the imposed constraints. Such constraints are derived from the training data, maintaining some relationship between features and outcomes. The most probable tag sequence for a given word sequence satisfies equation (1) and (2) respectively for HMM and ME model: Here, hi is the context for word wi. Since the basic bigram model of HMM as well as the equivalent ME models do not yield satisfactory accuracy, so the available resources like a morphological analyzer was used appropriately for better accuracy BIB004 . Three taggers have been implemented based on bigram HMM and ME model. The first tagger makes use of the supervised HMM model parameters and is named as HMM-S, the second tagger uses the semi supervised model parameters and is called HMM-SS. The third tagger is based on ME model and is used to find the most probable tag sequence for a given sequence of words. Morphological Analyzer was also used to further improve the accuracy of the tagger and integrated the morphological information with the model  . They assumed that the POS-tag of a word w can take values from the set TMA(w), where TMA(w) is computed by the Morphological Analyzer. The size of TMA (w) is much smaller than T. Thus, they have a restricted choice of tags as well as tag sequences for a given sentence. Since the correct tag t for w is always in TMA(w) (assuming that the morphological analyzer is complete), it is always possible to find out the correct tag sequence for a sentence even after applying the morphological restriction. Due to a much reduced set of possibilities, this model is expected to perform better for both the HMM (HMM-S and HMM-SS) and ME models even when only a small amount of labeled training text is available. They called these new models HMM-S+MA, HMM-SS+ MA and ME+MA BIB004 . To further improve the proposed models, the suffix information was also taken into consideration. Suffix information has been used during smoothing of emission probabilities for HMM models, whereas for ME models, suffix information is used as another type of feature BIB001 . The model with suffix information are denoted a '+suf' marker. Thus, They the new model are -HMM-S+suf, HMMS+suf+MA, HMM-SS+suf etc BIB004 .