A Hierarchical Framework for Relation Extraction with Reinforcement Learning (1811.03925v1)

Abstract: Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation. We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types. The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations. Our model was evaluated on public datasets collected via distant supervision, and results show that it gains better performance than existing methods and is more powerful for extracting overlapping relations.

• The paper introduces a hierarchical reinforcement learning framework that integrates relation detection with entity extraction.
• It employs two-tier reinforcement learning policies to effectively handle overlapping relations, achieving notable precision and recall improvements on datasets like NYT10 and NYT11.
• The study offers practical insights for constructing robust knowledge bases and sets a foundation for future research in hierarchical multitask learning.

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

This paper introduces a novel hierarchical reinforcement learning (HRL) framework designed to tackle relation extraction tasks by jointly modeling entity mentions and relation types. Diverging from traditional models that sequentially handle entity recognition followed by relation classification, this framework addresses the interdependence of these two processes and utilizes a two-level HRL strategy to optimize the extraction of overlapping relations often encountered in natural language processing tasks.

Framework and Methodology

The proposed framework operates in a hierarchical manner, implementing two distinct reinforcement learning (RL) policies: a high-level process for relation detection and a low-level process for entity extraction. The process commences with the high-level RL policy, which detects relation indicators within text sequences. Upon location of a relation type, the framework activates the low-level policy to extract the respective source and target entities involved in the detected relations.

High-Level RL Process

The high-level RL policy selects options (or relation types) from a defined set. The options include specific relation types and a unique no-relation type (NR). Upon detecting a potential relation at a given sentence position, the RL agent initiates a low-level process to identify participating entities. This high-level process utilizes a state representation that includes the current word's hidden state, the last selected option's vector, and the latent state from the preceding timestep. The use of these features in conjunction allows the policy to dynamically determine relation indicators with a higher accuracy than traditional models which rely on predetermined cues (e.g., relation triggers like verbs or prepositions).

Low-Level RL Process

The low-level RL policy engages in sequential labeling of entity mentions, assigning each word a unique tag pertinent to its role in a relation (i.e., source, target, or non-associated). The state representation for the low-level policy consists of the current word's hidden state, the previous action vector, and a context vector derived from the high-level state. This meticulously formulated state space empowers the model to accommodate multiple, complex relations, thereby handling overlapping relations with greater efficacy.

Experimental Results and Implications

The HRL framework was evaluated against both noisy and manually annotated datasets, NYT10 and NYT11, respectively. Significant numerical improvements were observed in precision and recall, particularly for sentences with overlapping relational structures. Notably, on the NYT10 dataset, the framework attained an $F_1$ score of 0.644, demonstrating superior handling of noisy data compared to conventional methods.

The robust performance of this framework can be attributed to its capacity to disentangle and sequentially process overlapping relations. This capability is substantiated by additional experiments on artificially constructed datasets containing overlapping relations, where the model surpassed state-of-the-art techniques, including neural joint extraction models, which typically falter under similar conditions.

Practical and Theoretical Implications

Practically speaking, this HRL framework facilitates the construction of comprehensive knowledge bases by extracting reliable relation triples from abundant yet unstructured text data. Its adeptness in dealing with overlapping relations—frequently found in domains like biomedical text mining—widens its applicability to various AI-driven knowledge acquisition tasks.

From a theoretical standpoint, the employment of HRL introduces an innovative paradigm for joint extraction tasks, possibly influential for future research in hierarchical multitask learning. The implicit feedback loops between the high-level relation detection and the low-level entity extraction processes highlight a novel approach to capturing task interdependencies in AI systems.

Future Directions

Further research could explore expanding this framework to encapsulate broader tasks such as event extraction or document-level relation discovery. Additionally, integrating unsupervised or semi-supervised learning aspects might enhance the model's adaptability and reduce dependency on large annotated datasets. Such developments could successfully harness this framework's full potential, paving the path for advancements in automated information extraction systems.

In conclusion, the hierarchical reinforcement learning framework proposed by the authors represents a significant advancement in the domain of relation extraction, delivering both practical efficacy and opening new avenues for exploratory research and application in artificial intelligence.