26,729 research outputs found
Transfer Learning for Scientific Data Chain Extraction in Small Chemical Corpus with BERT-CRF Model
Computational chemistry develops fast in recent years due to the rapid growth
and breakthroughs in AI. Thanks for the progress in natural language
processing, researchers can extract more fine-grained knowledge in publications
to stimulate the development in computational chemistry. While the works and
corpora in chemical entity extraction have been restricted in the biomedicine
or life science field instead of the chemistry field, we build a new corpus in
chemical bond field annotated for 7 types of entities: compound, solvent,
method, bond, reaction, pKa and pKa value. This paper presents a novel BERT-CRF
model to build scientific chemical data chains by extracting 7 chemical
entities and relations from publications. And we propose a joint model to
extract the entities and relations simultaneously. Experimental results on our
Chemical Special Corpus demonstrate that we achieve state-of-art and
competitive NER performance
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
An Event Network for Exploring Open Information
In this paper, an event network is presented for exploring open information,
where linguistic units about an event are organized for analysing. The process
is divided into three steps: document event detection, event network
construction and event network analysis. First, by implementing event detection
or tracking, documents are retrospectively (or on-line) organized into document
events. Secondly, for each of the document event, linguistic units are
extracted and combined into event networks. Thirdly, various analytic methods
are proposed for event network analysis. In our application methodologies are
presented for exploring open information
One for All: Towards Language Independent Named Entity Linking
Entity linking (EL) is the task of disambiguating mentions in text by
associating them with entries in a predefined database of mentions (persons,
organizations, etc). Most previous EL research has focused mainly on one
language, English, with less attention being paid to other languages, such as
Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent
Entity Linking system, which provides an EL framework which, once trained on
one language, works remarkably well on a number of different languages without
change. LIEL makes a joint global prediction over the entire document,
employing a discriminative reranking framework with many domain and
language-independent feature functions. Experiments on numerous benchmark
datasets, show that the proposed system, once trained on one language, English,
outperforms several state-of-the-art systems in English (by 4 points) and the
trained model also works very well on Spanish (14 points better than a
competitor system), demonstrating the viability of the approach.Comment: Association for Computational Linguistics (ACL), 201
Supervised and Unsupervised Ensembling for Knowledge Base Population
We present results on combining supervised and unsupervised methods to
ensemble multiple systems for two popular Knowledge Base Population (KBP)
tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and
Linking (TEDL). We demonstrate that our combined system along with auxiliary
features outperforms the best performing system for both tasks in the 2015
competition, several ensembling baselines, as well as the state-of-the-art
stacking approach to ensembling KBP systems. The success of our technique on
two different and challenging problems demonstrates the power and generality of
our combined approach to ensembling
Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)
Recent research has shown great progress on fine-grained entity typing. Most
existing methods require pre-defining a set of types and training a multi-class
classifier from a large labeled data set based on multi-level linguistic
features. They are thus limited to certain domains, genres and languages. In
this paper, we propose a novel unsupervised entity typing framework by
combining symbolic and distributional semantics. We start from learning general
embeddings for each entity mention, compose the embeddings of specific contexts
using linguistic structures, link the mention to knowledge bases and learn its
related knowledge representations. Then we develop a novel joint hierarchical
clustering and linking algorithm to type all mentions using these
representations. This framework doesn't rely on any annotated data, predefined
typing schema, or hand-crafted features, therefore it can be quickly adapted to
a new domain, genre and language. Furthermore, it has great flexibility at
incorporating linguistic structures (e.g., Abstract Meaning Representation
(AMR), dependency relations) to improve specific context representation.
Experiments on genres (news and discussion forum) show comparable performance
with state-of-the-art supervised typing systems trained from a large amount of
labeled data. Results on various languages (English, Chinese, Japanese, Hausa,
and Yoruba) and domains (general and biomedical) demonstrate the portability of
our framework
Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach
Relation extraction is the task of identifying predefined relationship
between entities, and plays an essential role in information extraction,
knowledge base construction, question answering and so on. Most existing
relation extractors make predictions for each entity pair locally and
individually, while ignoring implicit global clues available across different
entity pairs and in the knowledge base, which often leads to conflicts among
local predictions from different entity pairs. This paper proposes a joint
inference framework that employs such global clues to resolve disagreements
among local predictions. We exploit two kinds of clues to generate constraints
which can capture the implicit type and cardinality requirements of a relation.
Those constraints can be examined in either hard style or soft style, both of
which can be effectively explored in an integer linear program formulation.
Experimental results on both English and Chinese datasets show that our
proposed framework can effectively utilize those two categories of global clues
and resolve the disagreements among local predictions, thus improve various
relation extractors when such clues are applicable to the datasets. Our
experiments also indicate that the clues learnt automatically from existing
knowledge bases perform comparably to or better than those refined by human.Comment: to appear in Artificial Intelligenc
A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
We present a novel hierarchical distance-dependent Bayesian model for event
coreference resolution. While existing generative models for event coreference
resolution are completely unsupervised, our model allows for the incorporation
of pairwise distances between event mentions -- information that is widely used
in supervised coreference models to guide the generative clustering processing
for better event clustering both within and across documents. We model the
distances between event mentions using a feature-rich learnable distance
function and encode them as Bayesian priors for nonparametric clustering.
Experiments on the ECB+ corpus show that our model outperforms state-of-the-art
methods for both within- and cross-document event coreference resolution.Comment: 12 pages, 3 figure
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
In this paper, we consider the problem of open information extraction (OIE)
for extracting entity and relation level intermediate structures from sentences
in open-domain. We focus on four types of valuable intermediate structures
(Relation, Attribute, Description, and Concept), and propose a unified
knowledge expression form, SAOKE, to express them. We publicly release a data
set which contains more than forty thousand sentences and the corresponding
facts in the SAOKE format labeled by crowd-sourcing. To our knowledge, this is
the largest publicly available human labeled data set for open information
extraction tasks. Using this labeled SAOKE data set, we train an end-to-end
neural model using the sequenceto-sequence paradigm, called Logician, to
transform sentences into facts. For each sentence, different to existing
algorithms which generally focus on extracting each single fact without
concerning other possible facts, Logician performs a global optimization over
all possible involved facts, in which facts not only compete with each other to
attract the attention of words, but also cooperate to share words. An
experimental study on various types of open domain relation extraction tasks
reveals the consistent superiority of Logician to other states-of-the-art
algorithms. The experiments verify the reasonableness of SAOKE format, the
valuableness of SAOKE data set, the effectiveness of the proposed Logician
model, and the feasibility of the methodology to apply end-to-end learning
paradigm on supervised data sets for the challenging tasks of open information
extraction
Relation Extraction : A Survey
With the advent of the Internet, large amount of digital text is generated
everyday in the form of news articles, research publications, blogs, question
answering forums and social media. It is important to develop techniques for
extracting information automatically from these documents, as lot of important
information is hidden within them. This extracted information can be used to
improve access and management of knowledge hidden in large text corpora.
Several applications such as Question Answering, Information Retrieval would
benefit from this information. Entities like persons and organizations, form
the most basic unit of the information. Occurrences of entities in a sentence
are often linked through well-defined relations; e.g., occurrences of person
and organization in a sentence may be linked through relations such as employed
at. The task of Relation Extraction (RE) is to identify such relations
automatically. In this paper, we survey several important supervised,
semi-supervised and unsupervised RE techniques. We also cover the paradigms of
Open Information Extraction (OIE) and Distant Supervision. Finally, we describe
some of the recent trends in the RE techniques and possible future research
directions. This survey would be useful for three kinds of readers - i)
Newcomers in the field who want to quickly learn about RE; ii) Researchers who
want to know how the various RE techniques evolved over time and what are
possible future research directions and iii) Practitioners who just need to
know which RE technique works best in various settings
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