14,202 research outputs found
Exploring different representational units in English-to-Turkish statistical machine translation
We investigate different representational granularities for sub-lexical representation in statistical machine translation work from English to Turkish. We find that (i) representing both Turkish and English at the morpheme-level but with some selective morpheme-grouping on the Turkish side of the training data, (ii) augmenting the training data with “sentences” comprising only the content words of the original training data to bias root word alignment, (iii) reranking
the n-best morpheme-sequence outputs of the decoder with a word-based language
model, and (iv) using model iteration all provide a non-trivial improvement over
a fully word-based baseline. Despite our very limited training data, we improve from 20.22 BLEU points for our simplest model to 25.08 BLEU points for an improvement of 4.86 points or 24% relative
Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic
Language modeling for an inflected language
such as Arabic poses new challenges for speech recognition and
machine translation due to its rich morphology. Rich morphology
results in large increases in out-of-vocabulary (OOV) rate and
poor language model parameter estimation in the absence of large
quantities of data. In this study, we present a joint
morphological-lexical language model (JMLLM) that takes
advantage of Arabic morphology. JMLLM combines
morphological segments with the underlying lexical items and
additional available information sources with regards to
morphological segments and lexical items in a single joint model.
Joint representation and modeling of morphological and lexical
items reduces the OOV rate and provides smooth probability
estimates while keeping the predictive power of whole words.
Speech recognition and machine translation experiments in
dialectal-Arabic show improvements over word and morpheme
based trigram language models. We also show that as the
tightness of integration between different information sources
increases, both speech recognition and machine translation
performances improve
Use of Weighted Finite State Transducers in Part of Speech Tagging
This paper addresses issues in part of speech disambiguation using
finite-state transducers and presents two main contributions to the field. One
of them is the use of finite-state machines for part of speech tagging.
Linguistic and statistical information is represented in terms of weights on
transitions in weighted finite-state transducers. Another contribution is the
successful combination of techniques -- linguistic and statistical -- for word
disambiguation, compounded with the notion of word classes.Comment: uses psfig, ipamac
A Machine learning approach to POS tagging
We have applied inductive learning of statistical decision trees
and relaxation labelling to the Natural Language Processing (NLP)
task of morphosyntactic disambiguation (Part Of Speech Tagging).
The learning process is supervised and obtains a language
model oriented to resolve POS ambiguities. This model consists
of a set of statistical decision trees expressing distribution of
tags and words in some relevant contexts.
The acquired language models are complete enough to be directly
used as sets of POS disambiguation rules, and include more complex
contextual information than simple collections of n-grams usually
used in statistical taggers.
We have implemented a quite simple and fast tagger that has been
tested and evaluated on the Wall Street Journal (WSJ) corpus with
a remarkable accuracy.
However, better results can be obtained by translating the trees
into rules to feed a flexible relaxation labelling based tagger.
In this direction we describe a tagger which is able to use
information of any kind (n-grams, automatically acquired constraints,
linguistically motivated manually written constraints, etc.), and in
particular to incorporate the machine learned decision trees.
Simultaneously, we address the problem of tagging when only
small training material is available, which is crucial in any process
of constructing, from scratch, an annotated corpus. We show that quite
high accuracy can be achieved with our system in this situation.Postprint (published version
Mostly-Unsupervised Statistical Segmentation of Japanese Kanji Sequences
Given the lack of word delimiters in written Japanese, word segmentation is
generally considered a crucial first step in processing Japanese texts. Typical
Japanese segmentation algorithms rely either on a lexicon and syntactic
analysis or on pre-segmented data; but these are labor-intensive, and the
lexico-syntactic techniques are vulnerable to the unknown word problem. In
contrast, we introduce a novel, more robust statistical method utilizing
unsegmented training data. Despite its simplicity, the algorithm yields
performance on long kanji sequences comparable to and sometimes surpassing that
of state-of-the-art morphological analyzers over a variety of error metrics.
The algorithm also outperforms another mostly-unsupervised statistical
algorithm previously proposed for Chinese.
Additionally, we present a two-level annotation scheme for Japanese to
incorporate multiple segmentation granularities, and introduce two novel
evaluation metrics, both based on the notion of a compatible bracket, that can
account for multiple granularities simultaneously.Comment: 22 pages. To appear in Natural Language Engineerin
Lexicalization and Grammar Development
In this paper we present a fully lexicalized grammar formalism as a
particularly attractive framework for the specification of natural language
grammars. We discuss in detail Feature-based, Lexicalized Tree Adjoining
Grammars (FB-LTAGs), a representative of the class of lexicalized grammars. We
illustrate the advantages of lexicalized grammars in various contexts of
natural language processing, ranging from wide-coverage grammar development to
parsing and machine translation. We also present a method for compact and
efficient representation of lexicalized trees.Comment: ps file. English w/ German abstract. 10 page
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