92,912 research outputs found
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Semantic parsing shines at analyzing complex natural language that involves
composition and computation over multiple pieces of evidence. However, datasets
for semantic parsing contain many factoid questions that can be answered from a
single web document. In this paper, we propose to evaluate semantic
parsing-based question answering models by comparing them to a question
answering baseline that queries the web and extracts the answer only from web
snippets, without access to the target knowledge-base. We investigate this
approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional
language, and find that our model obtains reasonable performance (35 F1
compared to 41 F1 of state-of-the-art). We find in our analysis that our model
performs well on complex questions involving conjunctions, but struggles on
questions that involve relation composition and superlatives.Comment: *sem 201
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
The most approaches to Knowledge Base Question Answering are based on
semantic parsing. In this paper, we address the problem of learning vector
representations for complex semantic parses that consist of multiple entities
and relations. Previous work largely focused on selecting the correct semantic
relations for a question and disregarded the structure of the semantic parse:
the connections between entities and the directions of the relations. We
propose to use Gated Graph Neural Networks to encode the graph structure of the
semantic parse. We show on two data sets that the graph networks outperform all
baseline models that do not explicitly model the structure. The error analysis
confirms that our approach can successfully process complex semantic parses.Comment: Accepted as COLING 2018 Long Paper, 12 page
Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval
Semantic similarity based retrieval is playing an increasingly important role
in many IR systems such as modern web search, question-answering, similar
document retrieval etc. Improvements in retrieval of semantically similar
content are very significant to applications like Quora, Stack Overflow, Siri
etc. We propose a novel unsupervised model for semantic similarity based
content retrieval, where we construct semantic flow graphs for each query, and
introduce the concept of "soft seeding" in graph based semi-supervised learning
(SSL) to convert this into an unsupervised model.
We demonstrate the effectiveness of our model on an equivalent question
retrieval problem on the Stack Exchange QA dataset, where our unsupervised
approach significantly outperforms the state-of-the-art unsupervised models,
and produces comparable results to the best supervised models. Our research
provides a method to tackle semantic similarity based retrieval without any
training data, and allows seamless extension to different domain QA
communities, as well as to other semantic equivalence tasks.Comment: Published in Proceedings of the 2017 ACM Conference on Information
and Knowledge Management (CIKM '17
A Diagram Is Worth A Dozen Images
Diagrams are common tools for representing complex concepts, relationships
and events, often when it would be difficult to portray the same information
with natural images. Understanding natural images has been extensively studied
in computer vision, while diagram understanding has received little attention.
In this paper, we study the problem of diagram interpretation and reasoning,
the challenging task of identifying the structure of a diagram and the
semantics of its constituents and their relationships. We introduce Diagram
Parse Graphs (DPG) as our representation to model the structure of diagrams. We
define syntactic parsing of diagrams as learning to infer DPGs for diagrams and
study semantic interpretation and reasoning of diagrams in the context of
diagram question answering. We devise an LSTM-based method for syntactic
parsing of diagrams and introduce a DPG-based attention model for diagram
question answering. We compile a new dataset of diagrams with exhaustive
annotations of constituents and relationships for over 5,000 diagrams and
15,000 questions and answers. Our results show the significance of our models
for syntactic parsing and question answering in diagrams using DPGs
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