This paper describes the IIT Kharagpur dependency parsing system in CoNLL-2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We primarily focus on the low-resource languages (surprise languages). We have developed a framework to combine multiple treebanks to train parsers for low resource languages by a delexical-ization method. We have applied transformation on the source language tree-banks based on syntactic features of the low-resource language to improve performance of the parser. In the official evaluation , our system achieves macro-averaged LAS scores of 67.61 and 37.16 on the entire blind test data and the surprise language test data respectively.
We describe the CoNLL-2000 shared task: dividing text into syntactically related nonoverlapping groups of words, so-called text chunking. We give background information on the data sets, present a general overview of the systems that have taken part in the shared task and briefly discuss their performance.
We investigate the problem of complex answers in question answering. Complex answers
consist of several simple answers. We describe the online question answering system SHAPAQA,
and using data from this system we show that the problem of complex answers is quite
common. We define nine types of complex questions, and suggest two approaches, based on
answer frequencies, that allow question answering systems to tackle the problem.
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