10,110 research outputs found
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
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
Bayesian reordering model with feature selection
In phrase-based statistical machine translation systems, variation in grammatical structures between source and target languages can cause large movements of phrases. Modeling such movements is crucial in achieving translations of long sentences that appear natural in the target language. We explore generative learning approach to phrase reordering in Arabic to English. Formulating the reordering problem as a classification problem and using naive Bayes with feature selection, we achieve an improvement in the BLEU score over a lexicalized reordering model. The proposed model is compact, fast and scalable to a large corpus
Discovery of Linguistic Relations Using Lexical Attraction
This work has been motivated by two long term goals: to understand how humans
learn language and to build programs that can understand language. Using a
representation that makes the relevant features explicit is a prerequisite for
successful learning and understanding. Therefore, I chose to represent
relations between individual words explicitly in my model. Lexical attraction
is defined as the likelihood of such relations. I introduce a new class of
probabilistic language models named lexical attraction models which can
represent long distance relations between words and I formalize this new class
of models using information theory.
Within the framework of lexical attraction, I developed an unsupervised
language acquisition program that learns to identify linguistic relations in a
given sentence. The only explicitly represented linguistic knowledge in the
program is lexical attraction. There is no initial grammar or lexicon built in
and the only input is raw text. Learning and processing are interdigitated. The
processor uses the regularities detected by the learner to impose structure on
the input. This structure enables the learner to detect higher level
regularities. Using this bootstrapping procedure, the program was trained on
100 million words of Associated Press material and was able to achieve 60%
precision and 50% recall in finding relations between content-words. Using
knowledge of lexical attraction, the program can identify the correct relations
in syntactically ambiguous sentences such as ``I saw the Statue of Liberty
flying over New York.''Comment: dissertation, 56 page
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