By Anna Feldman
Whereas supervised corpus-based tools are hugely exact for various NLP tasks, together with morphological tagging, they're tricky to port to different languages simply because they require assets which are dear to create. consequently, many languages don't have any practical prospect for morpho-syntactic annotation within the foreseeable destiny. the tactic provided during this booklet goals to beat this challenge via considerably restricting the required information and as an alternative extrapolating the appropriate info from one other, comparable language. The technique has been verified on Catalan, Portuguese, and Russian. even if those languages are just really resource-poor, an analogous technique should be in precept utilized to any inflected language, so long as there's an annotated corpus of a similar language on hand. Time wanted for adjusting the method to a brand new language constitutes a fragment of the time wanted for structures with large, manually created assets: days rather than years. This booklet touches upon a couple of themes: typology, morphology, corpus linguistics, contrastive linguistics, linguistic annotation, computational linguistics and usual Language Processing (NLP). Researchers and scholars who're attracted to those clinical parts in addition to in cross-lingual reviews and purposes will drastically reap the benefits of this paintings. students and practitioners in desktop technological know-how and linguistics are the potential readers of this booklet.
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Additional resources for A Resource-Light Approach to Morpho-Syntactic Tagging
This raises the question whether such an approach could be used for other languages. Tagging inflectional languages with HMMs Literature on applications of the HMM algorithm to tagging inflectional languages is not available. The Cutting et al. (1992) tagger relies heavily on a lexicon for the target language and a suitably large sample of ordinary text. One could try to create such a lexicon using a morphological analyzer and then try unsupervised learning. But given that a morphological analyzer provides many spuriously ambiguous results, high tagging accuracy cannot be expected.
Support Vector Machines (SVMs), Vapnik (1998)). RL uses a binary classifier with higher capacity to revise the errors made by the stochastic model with lower capacity. During the training phase, a ranking is assigned to each class by the stochastic model for a training example. 5. A special approach to tagging highly infl. languages 25 given the example. Then the classes are checked in their ranked order. If the class is incorrect, the example is added to the training data for that class as a negative example, and the next ranked class is checked.
15). 3 Comparison of the tagging approaches Through these different approaches, two common points have emerged. First, for any given word, only a few tags are possible, a list of which can be found either in a dictionary or through a morphological analysis of the word. Chapter 2. Common tagging techniques 20 Second, when a word has several possible tags, the correct tag can generally be chosen from the local context, using contextual rules that define the valid sequences of tags. These rules may be given different priorities so that a selection can be made even when several rules apply.
A Resource-Light Approach to Morpho-Syntactic Tagging by Anna Feldman