Relation Extraction Exploiting Full Dependency Forests (original) (raw)
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Minimally Supervised Domain-Adaptive Parse Reranking for Relation Extraction
International Conference on Parsing Technologies (IWPT), 2011
The paper demonstrates how the generic parser of a minimally supervised information extraction framework can be adapted to a given task and domain for relation extraction (RE). For the experiments a generic deep-linguistic parser was employed that works with a largely hand-crafted head-driven phrase structure grammar (HPSG) for English. The output of this parser is a list of n best parses selected and ranked by a MaxEnt parse-ranking component, which had been trained on a more or less generic HPSG treebank. It will be shown how the estimated confidence of RE rules learned from the n best parses can be exploited for parse reranking. The acquired rerank-ing model improves the performance of RE in both training and test phases with the new first parses. The obtained significant boost of recall does not come from an overall gain in parsing performance but from an application-driven selection of parses that are best suited for the RE task. Since the readings best suited for successful rule extraction and instance extraction are often not the readings favored by a regular parser evaluation, generic parsing accuracy actually decreases. The novel method for task-specific parse reranking does not require any annotated data beyond the semantic seed, which is needed anyway for the RE task.
Exploring syntactic features for relation extraction using a convolution tree kernel
Proceedings of the main conference on …, 2006
This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extraction. Our study reveals that the syntactic structure features embedded in a parse tree are very effective for relation extraction and these features can be well captured by the convolution tree kernel. Evaluation on the ACE 2003 corpus shows that the convolution kernel over parse trees can achieve comparable performance with the previous best-reported feature-based methods on the 24 ACE relation subtypes. It also shows that our method significantly outperforms the previous two dependency tree kernels on the 5 ACE relation major types.
REflex: Flexible Framework for Relation Extraction in Multiple Domains
Proceedings of the 18th BioNLP Workshop and Shared Task, 2019
Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques. In this work, we build a unifying framework for RE, applying this on three highly used datasets (from the general, biomedical and clinical domains) with the ability to be extendable to new datasets. By performing a systematic exploration of modeling, pre-processing and training methodologies, we find that choices of preprocessing are a large contributor performance and that omission of such information can further hinder fair comparison. Other insights from our exploration allow us to provide recommendations for future research in this area.
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium on - NAACL '09, 2009
Relation extraction is a challenging task in natural language processing. Syntactic features are recently shown to be quite effective for relation extraction. In this paper, we generalize the state of the art syntactic convolution tree kernel introduced by Collins and Duffy. The proposed generalized kernel is more flexible and customizable, and can be conveniently utilized for systematic generation of more effective application specific syntactic sub-kernels. Using the generalized kernel, we will also propose a number of novel syntactic sub-kernels for relation extraction. These kernels show a remarkable performance improvement over the original Collins and Duffy kernel in the extraction of ACE-2005 relation types.
State-ofthe-Art Models for Relation Extraction
2021
The task of relation extraction aims at classifying the semantic relations between entities in a text. When coupled with named-entity recognition these can be used as the building blocks for an information extraction procedure that results in the construction of a Knowledge Graph. While many NLP libraries support named-entity recognition, there is no off-the-shelf solution for relation extraction. In this paper, we evaluate and compare several state-of-the-art approaches on a subset of the FewRel data set as well as a manually annotated corpus. The custom corpus contains six relations from the area of market research and is available for public use. Our approach provides guidance for the selection of models and training data for relation extraction in realworld projects.
Information Processing & Management, 2008
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper proposes to use the convolution kernel over parse trees together with support vector machines to model syntactic structured information for relation extraction. Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree. Our study reveals that the syntactic structured features embedded in a parse tree are very effective in relation extraction and can be well captured by the convolution tree kernel. Evaluation on the ACE benchmark corpora shows that using the convolution tree kernel only can achieve comparable performance with previous best-reported feature-based methods. It also shows that our method significantly outperforms previous two dependency tree kernels for relation extraction. Moreover, this paper proposes a composite kernel for relation extraction by combining the convolution tree kernel with a simple linear kernel. Our study reveals that the composite kernel can effectively capture both flat and structured features without extensive feature engineering, and easily scale to include more features. Evaluation on the ACE benchmark corpora shows that the composite kernel outperforms previous best-reported methods in relation extraction.
Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph
Proceedings of the AAAI Conference on Artificial Intelligence, 2020
A large majority of approaches have been proposed to leverage the dependency tree in the relation classification task. Recent works have focused on pruning irrelevant information from the dependency tree. The state-of-the-art Attention Guided Graph Convolutional Networks (AGGCNs) transforms the dependency tree into a weighted-graph to distinguish the relevance of nodes and edges for relation classification. However, in their approach, the graph is fully connected, which destroys the structure information of the original dependency tree. How to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenge in the relation classification task. In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree. We refer to this graph as a syntax-transport graph. We further propose a learna...
Relation Extraction using Explicit Context Conditioning
Proceedings of the 2019 Conference of the North
Relation Extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them. This works well for intrasentence RE and we call them first-order relations. However, this methodology can sometimes fail to capture complex and long dependencies. To address this, we hypothesize that at times two target entities can be explicitly connected via a context token. We refer to such indirect relations as second-order relations and describe an efficient implementation for computing them. These second-order relation scores are then combined with first-order relation scores. Our empirical results show that the proposed method leads to state-of-theart performance over two biomedical datasets.
Natural Language Computing, 2021
The task of relation extraction aims at classifying the semantic relations between entities in a text. When coupled with named-entity recognition these can be used as the building blocks for an information extraction procedure that results in the construction of a Knowledge Graph. While many NLP libraries support named-entity recognition, there is no off-the-shelf solution for relation extraction. In this paper, we evaluate and compare several state-of-the-art approaches on a subset of the FewRel data set as well as a manually annotated corpus. The custom corpus contains six relations from the area of market research and is available for public use. Our approach provides guidance for the selection of models and training data for relation extraction in realworld projects.
A Review of Relation Extraction
Many applications in information extraction, natural language understanding, information retrieval require an understanding of the semantic relations between entities. We present a comprehensive review of various aspects of the entity relation extraction task. Some of the most important supervised and semi-supervised classification approaches to the relation extraction task are covered in sufficient detail along with critical analyses. We also discuss extensions to higher-order relations. Evaluation methodologies for both supervised and semi-supervised methods are described along with pointers to the commonly used performance evaluation datasets. Finally, we also give short descriptions of two important applications of relation extraction, namely question answering and biotext mining.