46,798 research outputs found
Graph Neural Networks with Generated Parameters for Relation Extraction
Recently, progress has been made towards improving relational reasoning in
machine learning field. Among existing models, graph neural networks (GNNs) is
one of the most effective approaches for multi-hop relational reasoning. In
fact, multi-hop relational reasoning is indispensable in many natural language
processing tasks such as relation extraction. In this paper, we propose to
generate the parameters of graph neural networks (GP-GNNs) according to natural
language sentences, which enables GNNs to process relational reasoning on
unstructured text inputs. We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised
datasets show that our model achieves significant improvements compared to
baselines. We also perform a qualitative analysis to demonstrate that our model
could discover more accurate relations by multi-hop relational reasoning
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Keyphrase extraction from documents is useful to a variety of applications
such as information retrieval and document summarization. This paper presents
an end-to-end method called DivGraphPointer for extracting a set of diversified
keyphrases from a document. DivGraphPointer combines the advantages of
traditional graph-based ranking methods and recent neural network-based
approaches. Specifically, given a document, a word graph is constructed from
the document based on word proximity and is encoded with graph convolutional
networks, which effectively capture document-level word salience by modeling
long-range dependency between words in the document and aggregating multiple
appearances of identical words into one node. Furthermore, we propose a
diversified point network to generate a set of diverse keyphrases out of the
word graph in the decoding process. Experimental results on five benchmark data
sets show that our proposed method significantly outperforms the existing
state-of-the-art approaches.Comment: Accepted to SIGIR 201
Open-World Knowledge Graph Completion
Knowledge Graphs (KGs) have been applied to many tasks including Web search,
link prediction, recommendation, natural language processing, and entity
linking. However, most KGs are far from complete and are growing at a rapid
pace. To address these problems, Knowledge Graph Completion (KGC) has been
proposed to improve KGs by filling in its missing connections. Unlike existing
methods which hold a closed-world assumption, i.e., where KGs are fixed and new
entities cannot be easily added, in the present work we relax this assumption
and propose a new open-world KGC task. As a first attempt to solve this task we
introduce an open-world KGC model called ConMask. This model learns embeddings
of the entity's name and parts of its text-description to connect unseen
entities to the KG. To mitigate the presence of noisy text descriptions,
ConMask uses a relationship-dependent content masking to extract relevant
snippets and then trains a fully convolutional neural network to fuse the
extracted snippets with entities in the KG. Experiments on large data sets,
both old and new, show that ConMask performs well in the open-world KGC task
and even outperforms existing KGC models on the standard closed-world KGC task.Comment: 8 pages, accepted to AAAI 201
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
Knowledge bases are employed in a variety of applications from natural
language processing to semantic web search; alas, in practice their usefulness
is hurt by their incompleteness. Embedding models attain state-of-the-art
accuracy in knowledge base completion, but their predictions are notoriously
hard to interpret. In this paper, we adapt "pedagogical approaches" (from the
literature on neural networks) so as to interpret embedding models by
extracting weighted Horn rules from them. We show how pedagogical approaches
have to be adapted to take upon the large-scale relational aspects of knowledge
bases and show experimentally their strengths and weaknesses.Comment: presented at 2018 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2018), Stockholm, Swede
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