461 research outputs found
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
Relation classification is an important semantic processing task in the field
of natural language processing. In this paper, we propose the task of relation
classification for Chinese literature text. A new dataset of Chinese literature
text is constructed to facilitate the study in this task. We present a novel
model, named Structure Regularized Bidirectional Recurrent Convolutional Neural
Network (SR-BRCNN), to identify the relation between entities. The proposed
model learns relation representations along the shortest dependency path (SDP)
extracted from the structure regularized dependency tree, which has the
benefits of reducing the complexity of the whole model. Experimental results
show that the proposed method significantly improves the F1 score by 10.3, and
outperforms the state-of-the-art approaches on Chinese literature text.Comment: Accepted at NAACL HLT 2018. arXiv admin note: substantial text
overlap with arXiv:1711.0250
Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Relation classification is an important semantic processing task in the field
of natural language processing (NLP). In this paper, we present a novel model,
Structure Regularized Bidirectional Recurrent Convolutional Neural
Network(SR-BRCNN), to classify the relation of two entities in a sentence, and
the new dataset of Chinese Sanwen for named entity recognition and relation
classification. Some state-of-the-art systems concentrate on modeling the
shortest dependency path (SDP) between two entities leveraging convolutional or
recurrent neural networks. We further explore how to make full use of the
dependency relations information in the SDP and how to improve the model by the
method of structure regularization. We propose a structure regularized model to
learn relation representations along the SDP extracted from the forest formed
by the structure regularized dependency tree, which benefits reducing the
complexity of the whole model and helps improve the score by 10.3.
Experimental results show that our method outperforms the state-of-the-art
approaches on the Chinese Sanwen task and performs as well on the SemEval-2010
Task 8 dataset\footnote{The Chinese Sanwen corpus this paper developed and used
will be released in the further.Comment: arXiv admin note: text overlap with arXiv:1411.6243 by other author
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
We introduce an attention-based Bi-LSTM for Chinese implicit discourse
relations and demonstrate that modeling argument pairs as a joint sequence can
outperform word order-agnostic approaches. Our model benefits from a partial
sampling scheme and is conceptually simple, yet achieves state-of-the-art
performance on the Chinese Discourse Treebank. We also visualize its attention
activity to illustrate the model's ability to selectively focus on the relevant
parts of an input sequence.Comment: To appear at ACL2017, code available at
https://github.com/sronnqvist/discourse-ablst
Information Extraction from Scientific Literature for Method Recommendation
As a research community grows, more and more papers are published each year.
As a result there is increasing demand for improved methods for finding
relevant papers, automatically understanding the key ideas and recommending
potential methods for a target problem. Despite advances in search engines, it
is still hard to identify new technologies according to a researcher's need.
Due to the large variety of domains and extremely limited annotated resources,
there has been relatively little work on leveraging natural language processing
in scientific recommendation. In this proposal, we aim at making scientific
recommendations by extracting scientific terms from a large collection of
scientific papers and organizing the terms into a knowledge graph. In
preliminary work, we trained a scientific term extractor using a small amount
of annotated data and obtained state-of-the-art performance by leveraging large
amount of unannotated papers through applying multiple semi-supervised
approaches. We propose to construct a knowledge graph in a way that can make
minimal use of hand annotated data, using only the extracted terms,
unsupervised relational signals such as co-occurrence, and structural external
resources such as Wikipedia. Latent relations between scientific terms can be
learned from the graph. Recommendations will be made through graph inference
for both observed and unobserved relational pairs.Comment: Thesis Proposal. arXiv admin note: text overlap with arXiv:1708.0607
A recurrent neural model with attention for the recognition of Chinese implicit discourse relations
We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model’s ability to selectively focus on the relevant parts of an input sequence
Deep Learning for Sentiment Analysis : A Survey
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.Comment: 34 pages, 9 figures, 2 table
Conceptualization Topic Modeling
Recently, topic modeling has been widely used to discover the abstract topics
in text corpora. Most of the existing topic models are based on the assumption
of three-layer hierarchical Bayesian structure, i.e. each document is modeled
as a probability distribution over topics, and each topic is a probability
distribution over words. However, the assumption is not optimal. Intuitively,
it's more reasonable to assume that each topic is a probability distribution
over concepts, and then each concept is a probability distribution over words,
i.e. adding a latent concept layer between topic layer and word layer in
traditional three-layer assumption. In this paper, we verify the proposed
assumption by incorporating the new assumption in two representative topic
models, and obtain two novel topic models. Extensive experiments were conducted
among the proposed models and corresponding baselines, and the results show
that the proposed models significantly outperform the baselines in terms of
case study and perplexity, which means the new assumption is more reasonable
than traditional one.Comment: 7 page
Nonnegative Multi-level Network Factorization for Latent Factor Analysis
Nonnegative Matrix Factorization (NMF) aims to factorize a matrix into two
optimized nonnegative matrices and has been widely used for unsupervised
learning tasks such as product recommendation based on a rating matrix.
However, although networks between nodes with the same nature exist, standard
NMF overlooks them, e.g., the social network between users. This problem leads
to comparatively low recommendation accuracy because these networks are also
reflections of the nature of the nodes, such as the preferences of users in a
social network. Also, social networks, as complex networks, have many different
structures. Each structure is a composition of links between nodes and reflects
the nature of nodes, so retaining the different network structures will lead to
differences in recommendation performance. To investigate the impact of these
network structures on the factorization, this paper proposes four multi-level
network factorization algorithms based on the standard NMF, which integrates
the vertical network (e.g., rating matrix) with the structures of horizontal
network (e.g., user social network). These algorithms are carefully designed
with corresponding convergence proofs to retain four desired network
structures. Experiments on synthetic data show that the proposed algorithms are
able to preserve the desired network structures as designed. Experiments on
real-world data show that considering the horizontal networks improves the
accuracy of document clustering and recommendation with standard NMF, and
various structures show their differences in performance on these two tasks.
These results can be directly used in document clustering and recommendation
systems
Measuring Information Propagation in Literary Social Networks
We present the task of modeling information propagation in literature, in
which we seek to identify pieces of information passing from character A to
character B to character C, only given a description of their activity in text.
We describe a new pipeline for measuring information propagation in this domain
and publish a new dataset for speaker attribution, enabling the evaluation of
an important component of this pipeline on a wider range of literary texts than
previously studied. Using this pipeline, we analyze the dynamics of information
propagation in over 5,000 works of fiction, finding that information flows
through characters that fill structural holes connecting different communities,
and that characters who are women are depicted as filling this role much more
frequently than characters who are men.Comment: EMNLP 2020 long pape
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks
Computer vision with state-of-the-art deep learning models has achieved huge
success in the field of Optical Character Recognition (OCR) including text
detection and recognition tasks recently. However, Key Information Extraction
(KIE) from documents as the downstream task of OCR, having a large number of
use scenarios in real-world, remains a challenge because documents not only
have textual features extracting from OCR systems but also have semantic visual
features that are not fully exploited and play a critical role in KIE. Too
little work has been devoted to efficiently make full use of both textual and
visual features of the documents. In this paper, we introduce PICK, a framework
that is effective and robust in handling complex documents layout for KIE by
combining graph learning with graph convolution operation, yielding a richer
semantic representation containing the textual and visual features and global
layout without ambiguity. Extensive experiments on real-world datasets have
been conducted to show that our method outperforms baselines methods by
significant margins. Our code is available at
https://github.com/wenwenyu/PICK-pytorch.Comment: Accepted by ICPR2020. Code at
https://github.com/wenwenyu/PICK-pytorc
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