8,768 research outputs found
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction
In recent years there is a surge of interest in applying distant supervision
(DS) to automatically generate training data for relation extraction (RE). In
this paper, we study the problem what limits the performance of DS-trained
neural models, conduct thorough analyses, and identify a factor that can
influence the performance greatly, shifted label distribution. Specifically, we
found this problem commonly exists in real-world DS datasets, and without
special handing, typical DS-RE models cannot automatically adapt to this shift,
thus achieving deteriorated performance. To further validate our intuition, we
develop a simple yet effective adaptation method for DS-trained models, bias
adjustment, which updates models learned over the source domain (i.e., DS
training set) with a label distribution estimated on the target domain (i.e.,
test set). Experiments demonstrate that bias adjustment achieves consistent
performance gains on DS-trained models, especially on neural models, with an up
to 23% relative F1 improvement, which verifies our assumptions. Our code and
data can be found at
\url{https://github.com/INK-USC/shifted-label-distribution}.Comment: 13 pages: 10 pages paper, 3 pages appendix. Appears at EMNLP 201
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
Semantic relatedness based re-ranker for text spotting
Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness. The scenario is text spotting in the wild, where a text in an image (e.g. street sign, advertisement or bus destination) must be identified and recognized. Our goal is to improve the performance of vision systems by leveraging semantic information. Our rationale is that the text to be spotted is often related to the image context in which it appears (word pairs such as Delta–airplane, or quarters–parking are not similar, but are clearly related). We show how learning a word-to-word or word-to-sentence relatedness score can improve the performance of text spotting systems up to 2.9 points, outperforming other measures in a benchmark dataset.Peer ReviewedPostprint (author's final draft
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for
semantically parsing documents in specific domains. Basically, OONP reads a
document and parses it into a predesigned object-oriented data structure
(referred to as ontology in this paper) that reflects the domain-specific
semantics of the document. An OONP parser models semantic parsing as a decision
process: a neural net-based Reader sequentially goes through the document, and
during the process it builds and updates an intermediate ontology to summarize
its partial understanding of the text it covers. OONP supports a rich family of
operations (both symbolic and differentiable) for composing the ontology, and a
big variety of forms (both symbolic and differentiable) for representing the
state and the document. An OONP parser can be trained with supervision of
different forms and strength, including supervised learning (SL) ,
reinforcement learning (RL) and hybrid of the two. Our experiments on both
synthetic and real-world document parsing tasks have shown that OONP can learn
to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201
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