15,472 research outputs found
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
Automatic Synonym Discovery with Knowledge Bases
Recognizing entity synonyms from text has become a crucial task in many
entity-leveraging applications. However, discovering entity synonyms from
domain-specific text corpora (e.g., news articles, scientific papers) is rather
challenging. Current systems take an entity name string as input to find out
other names that are synonymous, ignoring the fact that often times a name
string can refer to multiple entities (e.g., "apple" could refer to both Apple
Inc and the fruit apple). Moreover, most existing methods require training data
manually created by domain experts to construct supervised-learning systems. In
this paper, we study the problem of automatic synonym discovery with knowledge
bases, that is, identifying synonyms for knowledge base entities in a given
domain-specific corpus. The manually-curated synonyms for each entity stored in
a knowledge base not only form a set of name strings to disambiguate the
meaning for each other, but also can serve as "distant" supervision to help
determine important features for the task. We propose a novel framework, called
DPE, to integrate two kinds of mutually-complementing signals for synonym
discovery, i.e., distributional features based on corpus-level statistics and
textual patterns based on local contexts. In particular, DPE jointly optimizes
the two kinds of signals in conjunction with distant supervision, so that they
can mutually enhance each other in the training stage. At the inference stage,
both signals will be utilized to discover synonyms for the given entities.
Experimental results prove the effectiveness of the proposed framework
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by
extracting entity relations from texts.However, it usually suffers from the
long-tail issue. The training data mainly concentrates on a few types of
relations, leading to the lackof sufficient annotations for the remaining types
of relations. In this paper, we propose a general approach to learn relation
prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction
by transferring knowledge from the relation types with sufficient trainingdata.
We learn relation prototypes as an implicit factor between entities, which
reflects the meanings of relations as well as theirproximities for transfer
learning. Specifically, we construct a co-occurrence graph from texts, and
capture both first-order andsecond-order entity proximities for embedding
learning. Based on this, we further optimize the distance from entity pairs
tocorresponding prototypes, which can be easily adapted to almost arbitrary RE
frameworks. Thus, the learning of infrequent or evenunseen relation types will
benefit from semantically proximate relations through pairs of entities and
large-scale textual information.We have conducted extensive experiments on two
publicly available datasets: New York Times and Google Distant
Supervision.Compared with eight state-of-the-art baselines, our proposed model
achieves significant improvements (4.1% F1 on average). Furtherresults on
long-tail relations demonstrate the effectiveness of the learned relation
prototypes. We further conduct an ablation study toinvestigate the impacts of
varying components, and apply it to four basic relation extraction models to
verify the generalization ability.Finally, we analyze several example cases to
give intuitive impressions as qualitative analysis. Our codes will be released
later
Improving Neural Relation Extraction with Implicit Mutual Relations
Relation extraction (RE) aims at extracting the relation between two entities
from the text corpora. It is a crucial task for Knowledge Graph (KG)
construction. Most existing methods predict the relation between an entity pair
by learning the relation from the training sentences, which contain the
targeted entity pair. In contrast to existing distant supervision approaches
that suffer from insufficient training corpora to extract relations, our
proposal of mining implicit mutual relation from the massive unlabeled corpora
transfers the semantic information of entity pairs into the RE model, which is
more expressive and semantically plausible. After constructing an entity
proximity graph based on the implicit mutual relations, we preserve the
semantic relations of entity pairs via embedding each vertex of the graph into
a low-dimensional space. As a result, we can easily and flexibly integrate the
implicit mutual relations and other entity information, such as entity types,
into the existing RE methods.
Our experimental results on a New York Times and another Google Distant
Supervision datasets suggest that our proposed neural RE framework provides a
promising improvement for the RE task, and significantly outperforms the
state-of-the-art methods. Moreover, the component for mining implicit mutual
relations is so flexible that can help to improve the performance of both
CNN-based and RNN-based RE models significant.Comment: 12 page
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