[PDF] Top 20 Unsupervised Part of Speech Acquisition for Resource Scarce Languages

... (mostly) unsupervised POS tagging without rely- ing on a POS ...an unsupervised manner using contrastive estimation, which seeks to move probability mass to a positive example e from its neighbors ... See full document

... of unsupervised ap- proaches to English POS tagging, we aim to inves- tigate whether such approaches, especially G&G’s fully-Bayesian approach, can deliver similar per- formance for Bengali, our representative ... See full document

... for resource-scarce ...Indic languages, Hindi and Telugu, be- cause of their resource ...of Speech (POS) Taggers, Parsers ...any resource-scarce language incorporating the ... See full document

... low-resource languages, spoken lan- guage resources are more likely to be an- notated with translations than with transcrip- ...Translated speech data is potentially valuable for documenting ... See full document

... transferring part-of-speech (POS) annotations from a resourced language (RL) towards an etymologically closely related non-resourced language (NRL), without using any bilingual ...two languages share ... See full document

... The representation of speech using a sequence of phonetic symbols is defined as transcription. Hindi has a phonetic writing system, i.e., there is very little distinction between its transcription and ... See full document

... 10 languages anno- tated 50,000 tokens per language (and an addi- tional 5,000 tokens for testing) on three levels, namely part of speech (POS), lemma, and mor- phological ... See full document

... case-insensitive languages, in- cluding the majority of semitic languages, Iranian languages, and Indian languages, is inherently more difficult than its English ...these languages is ... See full document

... Therefore, the problem of grounding the se- quence of states or cluster IDs to POS tags without using any linguistic resource remains unsolved. We formulate this task as a decipherment problem. Decipherment aims ... See full document

... two languages, but Duong et ...target languages are closely ...related languages, such as Feldman et ...low-resource languages can be built from scratch with only two hours of manual ... See full document

... In languages with productive inflectional morphology, several forms of the same lemma may not be seen in a training corpus and such word forms, when occurring in a new text, would be dealt with as unknown ... See full document

... In unsupervised learning, it is not always reasonable to assume that a large tag dictionary is available. To determine the effects of reduced or absent dictionary information, we ran a set of experiments inspired ... See full document

... speech tagging, the tags are conceived of as the hidden layer of the HMM and the tokens (each of which is associated with a type) as the visible layer. The emission probabilities are then the prob- abilities of ... See full document

... nine languages and apply it to test languages not in this ...training languages, we train a model where the parallel data for that language is excluded from the training ... See full document

... As linguistic models incorporate more subtle nuances of language and its structure, stan- dard inference techniques can fall behind. Of- ten, such models are tightly coupled such that they defy clever dynamic programming ... See full document

... mon part-of-speech tagging metrics bear a strong relationship to good grammar induction perfor- ...informative part-of-speech tags are important for good ... See full document

... phonemes, a phoneme substitution matrix has been con- structed consisting of a set of proposed phoneme substi- tutions for frequently used phonemes in most languages. These substitution phonemes are proposed based ... See full document

... While automatically induced text analysis tools use fewer resources, their accuracy lags behind that of more resource-intensive tools. One solution to the problem of error reduction on NLP tasks is to train ... See full document

... Automatic speech recognition and spoken dialogue systems have made great advances through the use of deep machine learning ...common languages, such as English. Conversely, research in minority ... See full document

... Systems using parallel corpora are not suitable for most low-resource languages, as the availability of such re- sources cannot be assumed. For our language pair Nynorsk–English we do not have access to a ... See full document