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Part of Speech (POS) tagging has a preliminary role in building natural language processing applications. This paper presents the development and evaluation of the first POS tagged corpus along with a Bi-directional long-short memory (BiLSTM) network based POS tagger for Shahmukhi (Western Punjabi) at this scale. A balanced corpus of 0.13 million words has been annotated which contains text from 14 different text domains. A Shahmukhi POS tagset has been devised by studying the applicability of the CLE Urdu POS tagset and tagging guidelines have also been designed for annotation. A multi-step corpus evaluation process has been employed for tagged corpus including grammar-based and n-gram based consistency evaluations. The average inter-annotator agreement for all domains is 95.35% along with an average Kappa coefficient of 0.94. The performance of the BiLSTM POS tagger has been compared with the well-known language independent TreeTagger and the Stanford POS tagger. The accuracy of the tagger has been further improved by employing transfer learning by training context-free (Word2Vec) and contextualized (ELMo) word representations on a corpus of 14.9 Shahmukhi words which has been collected from World Wide Web. The tagger performed with an f-score of 96.11 and the accuracy of 96.12%. For a morphologically-rich and low-resourced language, these POS tagging results are quite promising.

This article presents an analysis of experiments with statistical and neural parsing techniques for Urdu, a widely spoken South Asian language. We demonstrate state of the art constituency parsing results for an Urdu treebank. Urdu is a morphologically rich and is characterized by free word order. Language representation (e.g. input type, lemmatization, word clusters), part of speech tag set, phrase labels and the size of a training corpus are crucial for parsing such languages. In this article, probabilistic context-free grammars, data-oriented parsing, and recursive neural network based models have been experimented with several linguistic features which show improvements in the parsing results. Features include syntactic sub-categorization of POS tags, empirically learned horizontal and vertical markovizations and lexical head words. These features enable dependency information for case markers and add phrasal and lexical context to the parse trees. The data-oriented parsing and recursive neural network model give an f-score of 87.1 by considering gold POS tags in the test set, on textual input, they show a performance with f-scores of 83.4 and 84.2, respectively. To overcome the issue of data sparsity due to the morphological richness, lemmatization and unsupervised word clustering have been performed. A treebank should cover most probable word orders of the language so that models can learn various orders accurately. To analyze the order coverage of the treebank and learning capability of different parsers, a test set has been prepared conditioning different word orders. This test set is evaluated with the best performing parsing models and with gold POS tags, f-scores are above 90 and on textual input, the average f-score is 87.6.