![]() "HMM BASED POS TAGGER FOR HINDI" by Nisheeth Joshi, Hemant Darbari and Iti Mathur. "Hindi POS Tagger Using Naive Stemming : Harnessing Morphological Information Without Extensive Linguistic Knowledge" by Manish and Pushpak I have tried using Google Translator API to handle the "Unk" tags by translating and getting the tags and then appending it to the NLTK Indian Corpus which gave a pretty good result. we can also use Google translator to translate and get the tag.we can add more tagged sentences to NLTK hindi.pos. ![]() we can try handling the compound words as purposed by.we can use probability with freq for next word as purposed by.So we tend to get the tag "Unk" most of the time while tagging the words ex: ('वाशिंग', 'Unk'), ('मशीन', 'Unk'). The main issue here is that the nltk data is not complete. Tagged_words = (tnt_pos_tagger.tag(nltk.word_tokenize(text))) print(tagged_words) : Let's use the already tagged data which is given in nltk to train the data.įrom nltk.tag import tnt from rpus import indian train_data = indian.tagged_sents('hindi.pos') tnt_pos_tagger = tnt.TnT() tnt_pos_ain(train_data) So today we'll be using TNT tagger to tag Hindi words! S Phani Kumar Gadde, Meher Vijay Yeleti used CRF based tagger and Brants TnT (Brants, 2000), a HMM based tagger for hindi POS Tag where they got an acccuracy of 94.21%. while Nisheeth Joshi, Hemant Darbari and Iti Mathur also researched on Hindi POS using Hidden Markov Model with frequency count of two tags seen together in the corpus divided by the frequency count of the previous tag seen independently in the corpus. Manish and Pushpak researched on Hindi POS using a simple HMM based POS tagger with accuracy of 93.12%. Hindi Part of Speech Tagging is something that people are still doing research on as we have various techniques and libraries available for English Text and rarely for Hindi Text. Reminds you of school days? Okay now lets start with Hindi Part of Speech Tagging. Noun is divided into Proper Nouns, Common Nouns, Concrete Nouns etc. Most of it are further divided into sub-parts. There are eight main Parts of Speech: Nouns(naming word), Pronouns(replaces a noun), Adjectives(describing word), Verbs(action word), Adverbs(describes a verb), Prepositions(shows relationships), Conjunctions(joining word) and Interjections(Expressive word). Didn't we? But anyways let me give a brief explanation on it! ![]() Before going further on POS tagging, I am assuming that you all know about part of speech as we all have studied grammar during school. POS tagging is used mostly for Keyword Extractions, phrase extractions, Named Entity Recognition, etc. ![]() Part of speech plays a very major role in NLP task as it is important to know how a word is used in every sentences. Created Date: 28 Sept 2018 Hindi-POS-Tagging-and-Keyword-Extraction
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