Compound Sentence Segmentation and Sentence Boundary Detection in Urdu

The raw Urdu corpus comprises of irregular and large sentences which need to be properly segmented in order to make them useful in Natural Language Engineering (NLE). This makes the Compound Sentences Segmentation (CSS) timely and vital research topic. The existing online text processing tools are developed mostly for computationally developed languages such as English, Japanese and Spanish etc., where sentence segmentation is mostly done on the basis of delimiters. Our proposed approach uses special characters as sentence delimiters and computationally extracted sentence-endletters and sentence-end-words as identifiers for segmentation of large and compound sentences. The raw and unannotated input text is passed through preprocessing and word segmentation. Urdu word segmentation itself is a complex task including knotty problems such as space insertion and space deletion etc. Main and subordinate clauses are identified and marked for subsequent processing. The resultant text is further processed in order to identify, extract and then segment large as well as compound sentences into regular Urdu sentences. Urdu computational research is in its infancy. Our work is pioneering in Urdu CSS and results achieved by our proposed approach are promising. For experimentation, we used a general genre raw Urdu corpus containing 2616 sentences and 291503 words. We achieved 34% improvement in reduction of average sentence length from 111 w/s to 38 w/s (words per sentence). This increased the number of sentences by almost three times to 7536 shorter and computationally easy to manage sentences. Resultant text reliability and coherence are verified by Urdu language experts.