Part-Of-Speech Tagging Of Urdu in Limited Resources Scenario

We address the problem of Part-of-Speech (POS) tagging of Urdu. POS tagging is the process of assigning a part-of-speech or lexical class marker to each word in the given text. Tagging for natural languages is similar to tokenization and lexical analysis for computer languages, except that we encounter ambiguities which are to be resolved. It plays a fundamental role in various Natural Language Processing (NLP) applications such as word sense disambiguation, parsing, name entity recognition and chunking. POS tagging, particularly plays very important role in processing free-word-order languages because such languages have relatively complex morphological structure. Urdu is a morphologically rich language. Forms of the verb, as well as case, gender, and number are expressed by the morphology. It shares its morphology, phonology and grammatical structures with Hindi. It shares its vocabulary with Arabic, Persian, Sanskrit, Turkish and Pashto languages. Urdu is written using the Perso-Arabic script. POS tagging of Urdu is a necessary component for most NLP applications of Urdu. Development of an Urdu POS tagger will influence several pipelined modules of natural language understanding system, including machine translation; partial parsing and word sense disambiguation. Our objective is to develop a robust POS tagger for Urdu. We have worked on the automatic annotation of part-of-speech for Urdu. We have defined a tag-set for Urdu. We manually annotated a corpus of 10,000 sentences. We have used different machine learning methods, namely Hidden Markov Model (HMM), Maximum Entropy Model (ME) and Conditional Random Field (CRF). Further, to deal with a small-annotated corpus, we explored the use of semi-supervised learning by using an additional un-annotated corpus. We also explored the use of a dictionary to provide to us all possible POS labeling for a given word. Since Urdu is morphologically productive. Hence we augmented Hidden Markov Model, Maximum Entropy Model and Conditional Random Field with morphological features, word suffixes and POS categories of words to develop robust POS tagger for Urdu in the limited resources scenario

An Empirical Comparison of Parsing Methods for Stanford Dependencies

Stanford typed dependencies are a widely desired representation of natural language sentences, but parsing is one of the major computational bottlenecks in text analysis systems. In light of the evolving definition of the Stanford dependencies and developments in statistical dependency parsing algorithms, this paper revisits the question of Cer et al. (2010): what is the tradeoff between accuracy and speed in obtaining Stanford dependencies in particular? We also explore the effects of input representations on this tradeoff: part-of-speech tags, the novel use of an alternative dependency representation as input, and distributional representaions of words. We find that direct dependency parsing is a more viable solution than it was found to be in the past. An accompanying software release can be found at: http://www.ark.cs.cmu.edu/TBSDComment: 13 pages, 2 figure

Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary

Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language and its associated annotated corpora. However, parallel data is not readily available for many languages, limiting the applicability of these approaches. We address these drawbacks in our framework which takes advantage of cross-lingual word embeddings trained solely on a high coverage bilingual dictionary. We propose a novel neural network model for joint training from both sources of data based on cross-lingual word embeddings, and show substantial empirical improvements over baseline techniques. We also propose several active learning heuristics, which result in improvements over competitive benchmark methods.Comment: 5 pages with 2 pages reference. Accepted to appear in ACL 201