10,574 research outputs found
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
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
Diacritic Restoration and the Development of a Part-of-Speech Tagset for the MÄori Language
This thesis investigates two fundamental problems in natural language processing: diacritic restoration and part-of-speech tagging. Over the past three decades, statistical approaches to diacritic restoration and part-of-speech tagging have grown in interest as a consequence of the increasing availability of manually annotated training data in major languages such as English and French. However, these approaches are not practical for most minority languages, where appropriate training data is either non-existent or not publically available. Furthermore, before developing a part-of-speech tagging system, a suitable tagset is required for that language. In this thesis, we make the following contributions to bridge this gap:
Firstly, we propose a method for diacritic restoration based on naive Bayes classifiers that act at word-level. Classifications are based on a rich set of features, extracted automatically from training data in the form of diacritically marked text. This method requires no additional resources, which makes it language independent. The algorithm was evaluated on one language, namely MÄori, and an accuracy exceeding 99% was observed.
Secondly, we present our work on creating one of the necessary resources for the development of a part-of-speech tagging system in MÄori, that of a suitable tagset. The tagset described was developed in accordance with the EAGLES guidelines for morphosyntactic annotation of corpora, and was the result of in-depth analysis of the MÄori grammar
A Maximum-Entropy Partial Parser for Unrestricted Text
This paper describes a partial parser that assigns syntactic structures to
sequences of part-of-speech tags. The program uses the maximum entropy
parameter estimation method, which allows a flexible combination of different
knowledge sources: the hierarchical structure, parts of speech and phrasal
categories. In effect, the parser goes beyond simple bracketing and recognises
even fairly complex structures. We give accuracy figures for different
applications of the parser.Comment: 9 pages, LaTe
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
Memory-Based Lexical Acquisition and Processing
Current approaches to computational lexicology in language technology are
knowledge-based (competence-oriented) and try to abstract away from specific
formalisms, domains, and applications. This results in severe complexity,
acquisition and reusability bottlenecks. As an alternative, we propose a
particular performance-oriented approach to Natural Language Processing based
on automatic memory-based learning of linguistic (lexical) tasks. The
consequences of the approach for computational lexicology are discussed, and
the application of the approach on a number of lexical acquisition and
disambiguation tasks in phonology, morphology and syntax is described.Comment: 18 page
The interaction of knowledge sources in word sense disambiguation
Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results.
We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus.Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems
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