13,058 research outputs found
Improved Neural Relation Detection for Knowledge Base Question Answering
Relation detection is a core component for many NLP applications including
Knowledge Base Question Answering (KBQA). In this paper, we propose a
hierarchical recurrent neural network enhanced by residual learning that
detects KB relations given an input question. Our method uses deep residual
bidirectional LSTMs to compare questions and relation names via different
hierarchies of abstraction. Additionally, we propose a simple KBQA system that
integrates entity linking and our proposed relation detector to enable one
enhance another. Experimental results evidence that our approach achieves not
only outstanding relation detection performance, but more importantly, it helps
our KBQA system to achieve state-of-the-art accuracy for both single-relation
(SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.Comment: Accepted by ACL 2017 (updated for camera-ready
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering
Relation detection plays a crucial role in Knowledge Base Question Answering
(KBQA) because of the high variance of relation expression in the question.
Traditional deep learning methods follow an encoding-comparing paradigm, where
the question and the candidate relation are represented as vectors to compare
their semantic similarity. Max- or average- pooling operation, which compresses
the sequence of words into fixed-dimensional vectors, becomes the bottleneck of
information. In this paper, we propose to learn attention-based word-level
interactions between questions and relations to alleviate the bottleneck issue.
Similar to the traditional models, the question and relation are firstly
represented as sequences of vectors. Then, instead of merging the sequence into
a single vector with pooling operation, soft alignments between words from the
question and the relation are learned. The aligned words are subsequently
compared with the convolutional neural network (CNN) and the comparison results
are merged finally. Through performing the comparison on low-level
representations, the attention-based word-level interaction model (ABWIM)
relieves the information loss issue caused by merging the sequence into a
fixed-dimensional vector before the comparison. The experimental results of
relation detection on both SimpleQuestions and WebQuestions datasets show that
ABWIM achieves state-of-the-art accuracy, demonstrating its effectiveness.Comment: Paper submitted to Neurocomputing at 11.12.201
Using Context Information to Enhance Simple Question Answering
With the rapid development of knowledge bases(KBs),question
answering(QA)based on KBs has become a hot research issue. In this paper,we
propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus
answering single-relation factoid question. In both of two frameworks,we study
the effect of context information on the quality of QA,such as the entity's
notable type,out-degree. In the end-to-end framework,we combine char-level
encoding and self-attention mechanisms,using weight sharing and multi-task
strategies to enhance the accuracy of QA. Experimental results show that
context information can get better results of simple QA whether it is the
pipeline framework or the end-to-end framework. In addition,we find that the
end-to-end framework achieves results competitive with state-of-the-art
approaches in terms of accuracy and take much shorter time than them.Comment: under review World Wide Web Journa
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
A Restricted Visual Turing Test for Deep Scene and Event Understanding
This paper presents a restricted visual Turing test (VTT) for story-line
based deep understanding in long-term and multi-camera captured videos. Given a
set of videos of a scene (such as a multi-room office, a garden, and a parking
lot.) and a sequence of story-line based queries, the task is to provide
answers either simply in binary form "true/false" (to a polar query) or in an
accurate natural language description (to a non-polar query). Queries, polar or
non-polar, consist of view-based queries which can be answered from a
particular camera view and scene-centered queries which involves joint
inference across different cameras. The story lines are collected to cover
spatial, temporal and causal understanding of input videos. The data and
queries distinguish our VTT from recently proposed visual question answering in
images and video captioning. A vision system is proposed to perform joint video
and query parsing which integrates different vision modules, a knowledge base
and a query engine. The system provides unified interfaces for different
modules so that individual modules can be reconfigured to test a new method. We
provide a benchmark dataset and a toolkit for ontology guided story-line query
generation which consists of about 93.5 hours videos captured in four different
locations and 3,426 queries split into 127 story lines. We also provide a
baseline implementation and result analyses
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
An attention-based Bi-GRU-CapsNet model for hypernymy detection between compound entities
Named entities are usually composable and extensible. Typical examples are
names of symptoms and diseases in medical areas. To distinguish these entities
from general entities, we name them \textit{compound entities}. In this paper,
we present an attention-based Bi-GRU-CapsNet model to detect hypernymy
relationship between compound entities. Our model consists of several important
components. To avoid the out-of-vocabulary problem, English words or Chinese
characters in compound entities are fed into the bidirectional gated recurrent
units. An attention mechanism is designed to focus on the differences between
the two compound entities. Since there are some different cases in hypernymy
relationship between compound entities, capsule network is finally employed to
decide whether the hypernymy relationship exists or not. Experimental results
demonstrateComment: 5 pages, 3 figures. Accepted as short paper by 2018 International
Conference on Bioinformatics and Biomedicin
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines
Question Answering with Subgraph Embeddings
This paper presents a system which learns to answer questions on a broad
range of topics from a knowledge base using few hand-crafted features. Our
model learns low-dimensional embeddings of words and knowledge base
constituents; these representations are used to score natural language
questions against candidate answers. Training our system using pairs of
questions and structured representations of their answers, and pairs of
question paraphrases, yields competitive results on a competitive benchmark of
the literature
Learning Visual Knowledge Memory Networks for Visual Question Answering
Visual question answering (VQA) requires joint comprehension of images and
natural language questions, where many questions can't be directly or clearly
answered from visual content but require reasoning from structured human
knowledge with confirmation from visual content. This paper proposes visual
knowledge memory network (VKMN) to address this issue, which seamlessly
incorporates structured human knowledge and deep visual features into memory
networks in an end-to-end learning framework. Comparing to existing methods for
leveraging external knowledge for supporting VQA, this paper stresses more on
two missing mechanisms. First is the mechanism for integrating visual contents
with knowledge facts. VKMN handles this issue by embedding knowledge triples
(subject, relation, target) and deep visual features jointly into the visual
knowledge features. Second is the mechanism for handling multiple knowledge
facts expanding from question and answer pairs. VKMN stores joint embedding
using key-value pair structure in the memory networks so that it is easy to
handle multiple facts. Experiments show that the proposed method achieves
promising results on both VQA v1.0 and v2.0 benchmarks, while outperforms
state-of-the-art methods on the knowledge-reasoning related questions.Comment: Supplementary to CVPR 2018 versio
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