In this article, compound processing for translation into German in a factored statistical MT system is investigated. Compounds are handled by splitting them prior to training, and merging the parts after translation. I have explored eight merging strategies using different combinations of external knowledge sources, such as word lists, and internal sources that are carried through the translat...

When Part-of-Speech annotated data is scarce, e.g. for under-resourced languages, one can turn to cross-lingual transfer and crawled dictionaries to collect partially supervised data. We cast this problem in the framework of ambiguous learning and show how to learn an accurate history-based model. Experiments on ten languages show significant improvements over prior state of the art performance.

In this paper we present a Marathipart of speech tagger. It is morphologically rich language. it is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using Trigram Method. The main concept of Trigram is to explore the most likely POS for a token based on given information of previous two tags by calculating probabilities to determine ...

Neuro-imaging studies on reading different parts of speech (PoS) report somewhat mixed results, yet some of them indicate different activations with different PoS. This paper addresses the difficulty of using fMRI to discriminate between linguistic tokens in reading of running text because of low temporal resolution. We show that once we solve this problem, fMRI data contains a signal of PoS di...

With this paper is presented a system for Part of Speech Tagging, based on the Perceptron Algorithm. In the proposed framework, the order of the inference is not forced into a monotonic behavior (left-toright), but is learned together with the parameters of the local classifier. The system tested on the task of Italian POS Tagging at EVALITA 2009 obtained the second position, with a Tagging Acc...

This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors. The speed of tagging is also i...

We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-theart results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust agai...

This paper describes a Chinese part-ofspeech tagging system based on the maximum entropy model. It presents a novel two-stage approach to using the part-ofspeech tags of the words on both sides of the current word in Chinese part-of-speech tagging. The system is evaluated on four corpora at the Fourth SIGHAN Bakeoff in the close track of the Chinese part-ofspeech tagging task.

We discuss part-of-speech (POS) tagging in presence of large, fine-grained label sets using conditional random fields (CRFs). We propose improving tagging accuracy by utilizing dependencies within sub-components of the fine-grained labels. These sub-label dependencies are incorporated into the CRF model via a (relatively) straightforward feature extraction scheme. Experiments on five languages ...