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Building Watson: An Overview of the DeepQA Project
IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includesExpand
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Metaphor Detection with Cross-Lingual Model Transfer
TLDR
We show that it is possible to reliably discriminate whether a syntactic construction is meant literally or metaphorically using lexical semantic features of the words that participate in the construction. Expand
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A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering
TLDR
In this paper, we present an approach to address the answer sentence selection problem for question answering, by a combination of the stacked bidirectional LSTM model and keywords matching. Expand
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Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models
TLDR
We use a sentence level sequence to sequence neural model with a pointer network component to enable direct copying of words from input. Expand
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Structured retrieval for question answering
TLDR
We present an approach to retrieval for QA, applying structured retrieval techniques to the types of text annotations that QA systems use. Expand
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Semantic Extensions of the Ephyra QA System for TREC 2007
TLDR
We describe recent extensions to the Ephyra question answering (QA) system and their evaluation in the TREC 2007 QA track. Expand
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The Language Application Grid
TLDR
The Language Application LAPPS Grid is establishing a framework that enables language service discovery, composition, and reuse and promotes sustainability, manageability, usability, and interoperability of natural language Processing NLP components. Expand
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Controlled Language for Multilingual Document Production: Experience with Caterpillar Technical English 1
TLDR
Caterpillar uses Caterpillar Technical English (CTE), a controlled English system developed in conjunction with CarnegieMellon University’s Center forMachine Translation (CMT) andCarnegie Group Incorporated (CGI). Expand
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CMU OAQA at TREC 2016 LiveQA: An Attentional Neural Encoder-Decoder Approach for Answer Ranking
TLDR
In this paper, we present CMU’s question answering system that was evaluated in the TREC 2016 LiveQA Challenge. Expand
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Nicotine effects on default mode network during resting state
RationaleThe default mode network (DMN), one of several resting-state networks (RSN) in the brain, is thought to be involved in self-referential thought, awareness, and episodic memories. NicotineExpand
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