[Adapt] [Seminar] Knowledge Base Question Answering via Encoding of Complex Query Structures
kzhu at cs.sjtu.edu.cn
Wed Sep 12 13:02:10 CST 2018
By the way, every talk should include at least 2 quiz questions as before…
From: adapt-bounces at cs.sjtu.edu.cn [mailto:adapt-bounces at cs.sjtu.edu.cn] On Behalf Of zhiyi Luo
Sent: Wednesday, September 12, 2018 11:47 AM
Subject: Re: [Adapt] [Seminar] Knowledge Base Question Answering via Encoding of Complex Query Structures
I will give the second half of the seminar, talking about our work "ExtRA: Extracting Prominent Review Aspects from Customer Feedback" which is accepted by EMNLP 2018 as a long paper. In this paper, we propose a framework, ExtRA, for extracting the most prominent aspects of a given product type from textual reviews. ExtRA extracts the aspect candidates from text reviews based on a data-driven approach, builds the aspect taxonomy by leveraging WordNet, then ranks the aspect candidates using the taxonomy, finally generates the expected prominent aspects for the product. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.
Luo Kangqi <luo.kangqi at qq.com> 于2018年9月12日周三 上午11:17写道：
In our weekly seminar today, I'll give a talk on my paper "Knowledge Base Question Answering via Encoding of Complex Query Structures" accepted in EMNLP 2018.
The goal of KBQA is to answer natural language questions which ask existing facts of some specific entities in the knowledge base. We attempt to solve the KBQA problem in a more complex scenario, where multiple entities and relations are involved in one question. In this work, we encode the complex query structure of a question into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. Experimental results on multiple KBQA datasets proved the effectiveness of our approach.
Time: Sept 12nd, 5pm (Wed, today)
Venue: Room 3-517A
See you there!
Kangqi Luo, PhD Candidate
ADAPT Lab, Department of Computer Science
SEIEE 3-341, Shanghai Jiao Tong University,
No. 800 Dongchuan Road, Shanghai, China
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