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Minimally supervised induction of morphology through bitexts
textA knowledge of morphology can be useful for many natural language processing systems. Thus, much effort has been expended in developing accurate computational tools for morphology that lemmatize, segment and generate new forms. The most powerful and accurate of these have been manually encoded, such endeavors being without exception expensive and time-consuming. There have been consequently many attempts to reduce this cost in the development of morphological systems through the development of unsupervised or minimally supervised algorithms and learning methods for acquisition of morphology. These efforts have yet to produce a tool that approaches the performance of manually encoded systems.
Here, I present a strategy for dealing with morphological clustering and segmentation in a minimally supervised manner but one that will be more linguistically informed than previous unsupervised approaches. That is, this study will attempt to induce clusters of words from an unannotated text that are inflectional variants of each other. Then a set of inflectional suffixes by part-of-speech will be induced from these clusters. This level of detail is made possible by a method known as alignment and transfer (AT), among other names, an approach that uses aligned bitexts to transfer linguistic resources developed for one language–the source language–to another language–the target. This approach has a further advantage in that it allows a reduction in the amount of training data without a significant degradation in performance making it useful in applications targeted at data collected from endangered languages. In the current study, however, I use English as the source and German as the target for ease of evaluation and for certain typlogical properties of German. The two main tasks, that of clustering and segmentation, are approached as sequential tasks with the clustering informing the segmentation to allow for greater accuracy in morphological analysis.
While the performance of these methods does not exceed the current roster of unsupervised or minimally supervised approaches to morphology acquisition, it attempts to integrate more learning methods than previous studies. Furthermore, it attempts to learn inflectional morphology as opposed to derivational morphology, which is a crucial distinction in linguistics.Linguistic
Augmenting Translation Lexica by Learning Generalised Translation Patterns
Bilingual Lexicons do improve quality: of parallel corpora alignment, of newly extracted
translation pairs, of Machine Translation, of cross language information retrieval, among
other applications. In this regard, the first problem addressed in this thesis pertains to
the classification of automatically extracted translations from parallel corpora-collections
of sentence pairs that are translations of each other. The second problem is concerned
with machine learning of bilingual morphology with applications in the solution of first
problem and in the generation of Out-Of-Vocabulary translations.
With respect to the problem of translation classification, two separate classifiers for
handling multi-word and word-to-word translations are trained, using previously extracted
and manually classified translation pairs as correct or incorrect. Several insights
are useful for distinguishing the adequate multi-word candidates from those that are
inadequate such as, lack or presence of parallelism, spurious terms at translation ends
such as determiners, co-ordinated conjunctions, properties such as orthographic similarity
between translations, the occurrence and co-occurrence frequency of the translation
pairs. Morphological coverage reflecting stem and suffix agreements are explored as key
features in classifying word-to-word translations. Given that the evaluation of extracted
translation equivalents depends heavily on the human evaluator, incorporation of an
automated filter for appropriate and inappropriate translation pairs prior to human evaluation
contributes to tremendously reduce this work, thereby saving the time involved
and progressively improving alignment and extraction quality. It can also be applied
to filtering of translation tables used for training machine translation engines, and to
detect bad translation choices made by translation engines, thus enabling significative
productivity enhancements in the post-edition process of machine made translations.
An important attribute of the translation lexicon is the coverage it provides. Learning
suffixes and suffixation operations from the lexicon or corpus of a language is an extensively
researched task to tackle out-of-vocabulary terms. However, beyond mere words
or word forms are the translations and their variants, a powerful source of information
for automatic structural analysis, which is explored from the perspective of improving
word-to-word translation coverage and constitutes the second part of this thesis. In this
context, as a phase prior to the suggestion of out-of-vocabulary bilingual lexicon entries,
an approach to automatically induce segmentation and learn bilingual morph-like units by identifying and pairing word stems and suffixes is proposed, using the bilingual
corpus of translations automatically extracted from aligned parallel corpora, manually
validated or automatically classified. Minimally supervised technique is proposed to enable
bilingual morphology learning for language pairs whose bilingual lexicons are highly
defective in what concerns word-to-word translations representing inflection diversity.
Apart from the above mentioned applications in the classification of machine extracted
translations and in the generation of Out-Of-Vocabulary translations, learned bilingual
morph-units may also have a great impact on the establishment of correspondences of
sub-word constituents in the cases of word-to-multi-word and multi-word-to-multi-word
translations and in compression, full text indexing and retrieval applications
Corpus-based paradigm Selection for morphological entries
Volume: 4 Host publication title: Nealt Proceedings Series Vol. 4 Host publication sub-title: Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009Peer reviewe
Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review
Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519
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