MBT: A Memory-Based Part of Speech Tagger-Generator

We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger. Based on such a corpus, the tagger-generator automatically builds a tagger which is able to tag new text the same way, diminishing development time for the construction of a tagger considerably. Memory-based tagging shares this advantage with other statistical or machine learning approaches. Additional advantages specific to a memory-based approach include (i) the relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities, (iv) flexible integration of information in case representations, (v) its non-parametric nature, (vi) reasonably good results on unknown words without morphological analysis, and (vii) fast learning and tagging. In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.} The use of IGTree has as additional advantage that optimal context size for disambiguation is dynamically computed.Comment: 14 pages, 2 Postscript figure

Statistical Function Tagging and Grammatical Relations of Myanmar Sentences

This paper describes a context free grammar (CFG) based grammatical relations for Myanmar sentences which combine corpus-based function tagging system. Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system. Function tagging is a pre-processing step to show grammatical relations of Myanmar sentences. In the task of function tagging, which tags the function of Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information, we use Naive Bayesian theory to disambiguate the possible function tags of a word. We apply context free grammar (CFG) to find out the grammatical relations of the function tags. We also create a functional annotated tagged corpus for Myanmar and propose the grammar rules for Myanmar sentences. Experiments show that our analysis achieves a good result with simple sentences and complex sentences.Comment: 16 pages, 7 figures, 8 tables, AIAA-2011 (India). arXiv admin note: text overlap with arXiv:0912.1820 by other author

Domain-specific language models and lexicons for tagging

AbstractAccurate and reliable part-of-speech tagging is useful for many Natural Language Processing (NLP) tasks that form the foundation of NLP-based approaches to information retrieval and data mining. In general, large annotated corpora are necessary to achieve desired part-of-speech tagger accuracy. We show that a large annotated general-English corpus is not sufficient for building a part-of-speech tagger model adequate for tagging documents from the medical domain. However, adding a quite small domain-specific corpus to a large general-English one boosts performance to over 92% accuracy from 87% in our studies. We also suggest a number of characteristics to quantify the similarities between a training corpus and the test data. These results give guidance for creating an appropriate corpus for building a part-of-speech tagger model that gives satisfactory accuracy results on a new domain at a relatively small cost

Tagging the Teleman Corpus

Experiments were carried out comparing the Swedish Teleman and the English Susanne corpora using an HMM-based and a novel reductionistic statistical part-of-speech tagger. They indicate that tagging the Teleman corpus is the more difficult task, and that the performance of the two different taggers is comparable.Comment: 14 pages, LaTeX, to appear in Proceedings of the 10th Nordic Conference of Computational Linguistics, Helsinki, Finland, 199

Chinese unknown word identification as known word tagging

This paper presents a tagging approach to Chinese unknown word identification based on lexicalized hidden Markov models (LHMMs). In this work, Chinese unknown word identification is represented as a tagging task on a sequence of known words by introducing word-formation patterns and part-of-speech. Based on the lexicalized HMMs, a statistical tagger is further developed to assign each known word an appropriate tag that indicates its pattern in forming a word and the part-of-speech of the formed word. The experimental results on the Peking University corpus indicate that the use of lexicalization technique and the introduction of part-of-speech are helpful to unknown word identification. The experiment on the SIGHAN-PK open test data also shows that our system can achieve state-of-art performance.published_or_final_versio

Comparion of different POS tagging technique (N-Gram, HMM and Brill's tagger) for Bangla

Includes bibliographical references (page 6-7).There are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amount of annotated training corpus to tag properly. At this initial stage of POS-tagging for Bangla, we have very limited resource of annotated corpus. We tried to see which technique maximizes the performance with this limited resource. We also checked the performance for English and tried to conclude how these techniques might perform if we can manage a substantial amount of annotated corpus.Naushad UzZamanFahim Muhammad HasanMumit Kha