The main goal of this work is the implementation of a new tool for the Amazigh part of speech tagging using Markov Models and decision trees. After studying different approaches and problems of part of speech tagging, we have implemented a tagging system based on TreeTagger a generic stochastic tagging tool, very popular for its efficiency. We have gathered a working corpus, large enough to ens...

Part-of-speech tagging is the process of marking up the words in a text (corpus) as corresponding to a particular part of speech, based on both its definition, as well as its context —i.e. relationship with adjacent and related words in a phrase, sentence, or paragraph. Part-of-Speech (POS) tagging is the process of assigning the appropriate part of speech or lexical category to each word in a ...

Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTMRNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or handwritten documents. In this study, we propose to use BLSTM-RNN for a unified tagging solution that can be applied to various tagging tasks including partof-speech tagging, chunking and named entity recognition. Ins...

A parts of speech (POS) tagging system using neural networks has been developed by Ma and colleagues. This system can tag unlearned data with a much higher accuracy than that of the Hidden Markov Model (HMM), which is the most popular method of POS tagging. It does so by learning a small Thai corpus on the order of 10,000 words that are ambiguous as to their POSs. However, the threelayer percep...

We present Global-Local POS tagging, a framework to train generative stochastic Part-of-Speech models on large corpora. Global Taggers offer several advantages over their counter parts trained on small, curated corpus, including the ability to automatically extend and update their models to new text. Global Taggers also avoid a fundamental limitation of current models, whose performance heavily...

this study was intended first to categorize the l2 learners in terms of their learning style preferences and second to investigate if their learning preferences are related to lexical inferencing. moreover, strategies used for lexical inferencing and text related issues of text density and parts of speech were studied to determine their moderating effects and the best predictors of lexical infe...

We report on an effort to build a corpus of Modern Hebrew tagged with parts of speech and morphology. We designed a tagset specific to Hebrew while focusing on four aspects: the tagset should be consistent with common linguistic knowledge; there should be maximal agreement among taggers as to the tags assigned to maintain consistency; the tagset should be useful for machine taggers and learning...

We used four Part-of-Speech taggers, which are available for research purposes and were originally trained on text to tag a corpus of transcribed multiparty spoken dialogues. The assigned tags were then manually corrected. The correction was first used to evaluate the four taggers, then to retrain them. Despite limited resources in time, money and annotators we reached results comparable to tho...