By Anna Feldman
Whereas supervised corpus-based equipment are hugely actual for various NLP tasks, together with morphological tagging, they're tough to port to different languages simply because they require assets which are dear to create. for that reason, many languages haven't any lifelike prospect for morpho-syntactic annotation within the foreseeable destiny. the tactic provided during this booklet goals to beat this challenge by way of considerably restricting the mandatory information and as an alternative extrapolating the suitable details from one other, similar language. The process has been proven on Catalan, Portuguese, and Russian. even supposing those languages are just particularly resource-poor, a similar 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 procedure to a brand new language constitutes a fragment of the time wanted for structures with wide, manually created assets: days rather than years. This ebook touches upon a couple of themes: typology, morphology, corpus linguistics, contrastive linguistics, linguistic annotation, computational linguistics and common Language Processing (NLP). Researchers and scholars who're drawn to those clinical parts in addition to in cross-lingual experiences and purposes will drastically make the most of this paintings. students and practitioners in desktop technological know-how and linguistics are the potential readers of this e-book.
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Additional resources for A Resource-Light Approach to Morpho-Syntactic Tagging
So, the determining context for deciding on a tag is the space of the previous n tags (n=2, in the case of a second order Markov model). The methods differ, however, in the way the transition probability p(tn |tn−2tn−1 ) is estimated. N-gram taggers often estimate the probability using the maximum likelihood principle, as mentioned above. Unlike those approaches, TreeTagger constructs a binary-branching decision tree. The binary tree is built recursively from a training set of trigrams. The nodes of the tree correspond to questions (or tests) about the previous one or two tags.
Can’ can be an auxiliary, a noun, and a verb). Still, many of these ambiguous tokens are easy to disambiguate, since the various tags associated with a word are not equally likely. In contrast, languages with rich morphologies are more challenging. Most Russian nouns, for instance, have singular and plural forms in all six cases (nominative, accusative, genitive, dative, locative, and instrumental). Most adjectives (at least potentially) form all three genders (masculine, feminine and neuter), both numbers (singular and plural), all six cases, all three degrees of comparison, and can be either of positive or negative polarity.
3). ). The analyzer is based on a lexicon containing about 228K lemmata and it can analyze about 20M word forms. 25% on the full tag. 2 Other experiments Finally, some experiments combine the exponential model described above with various other learning algorithms to improve tagging results. Hajiˇc et al. (2001) describe a hybrid system (applied to Czech) which combines the strength of manual rule-writing and statistical learning, obtaining results superior to both methods if applied separately.
A Resource-Light Approach to Morpho-Syntactic Tagging by Anna Feldman
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