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LEVERAGING DEPENDENCY STRUCTURE FOR INFERENCE COMPUTATION, SUMMARIZATION, AND COMPREHENSIONAuthor: Elizabeth george Date: 2020-07-08 Report no: IIIT/TH/2020/67 Advisor:Radhika Mamidi AbstractIn this research work, we present three syntax-based computational methods that use the dependency structure of sentences for inference generation, summarization, and machine reading comprehension. Besides, we present an introductory dataset to aid the production of inferences from a response in a context. The methods described in this thesis utilize the dependency structure obtained from the parser that represents the dependency relations existing between the words of the sentence. This work explains how these relations are utilized to compute inferences from utterances, summarize documents, and answer questions based on context passages. The first method describes computing pragmatic inferences from news headlines that are stand-alone units of text carrying no context information. The different types of pragmatic inferences are entailment, presupposition, and implicature. News headlines are some minimal utterances that contain maximum information for which the speaker is the news editor, and the receiver is the newsreader. The inferences, such as presuppositions and conventional implicatures that are independent of the context, depend mainly on syntax. Hence, this approach uses dependency trees of the headlines to find the syntactic structure and to compute inferences out of them. The generated inferences about the news headlines could be useful for assessing the impact of the same on readers, including children. Utterances do not frequently exist in isolation, like in news headlines. Hence, we created a minimal dataset with implicated meanings of utterances in a context. In this work, 1k context-response utterance dialogue pairs having implicatures for the response utterance are collected and annotated with their implicated meanings. Since dialogues with implicatures are not easy to obtain, the data collection methods, challenges, findings, and sources of dialogue data of this genre are also explained. This dataset and further additions to it would improve the understanding capability of the machine and reduce the amount of conversational failures in human-computer interactions. The second method describes summarizing a passage based on syntax and dependency structure. The extractive summary of the passage is obtained by finding overlapping sentences from the dependency graph constructed by attaching the dependency trees of individual sentences in the passage and simultaneously prioritizing content word nodes such as verbs, nouns, adjectives, and adverbs, and discarding translative nodes such as prepositions and articles. The third method uses dependency trees to simulate a human approach while answering a question about a passage. While reading a passage to answer a question, humans usually try to identify the verb of action in the question and find the sentences with the same or synonymous verbs as the candidate answers. Simulating the human approach of finding the same or similar verb in the passage comparing to the verb in the question is followed for comprehending the passage to answer a question. The verb nodes of the dependency trees are compared to achieve this. Full thesis: pdf Centre for Language Technologies Research Centre |
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