Cross-Sentence N-ary Relation Extraction with Graph LSTMs (1708.03743v1)

Abstract: Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.

Cross-Sentence $N$-ary Relation Extraction with Graph LSTMs

The paper "Cross-Sentence $N$-ary Relation Extraction with Graph LSTMs" addresses the growing need to extract complex relational data across sentence boundaries in high-value domains like biomedicine. Traditional relation extraction focuses primarily on binary relationships within single sentences, thereby limiting the potential for capturing complete information. This paper proposes a framework leveraging Graph Long Short-Term Memory networks (Graph LSTMs) to tackle the extraction of $n$-ary relations that span multiple sentences.

Framework and Methodology

The authors propose using Graph LSTMs, a novel approach that extends traditional LSTM architectures to handle graph-structured data. This graph formulation allows the model to incorporate various dependencies, such as sequential, syntactic, and discourse relations, enabling the capture of both intra-sentential and inter-sentential dependencies. Such an approach assists in establishing robust contextual representations of entities, thus facilitating the relation classification task. The framework streamlines the extraction of relations with arbitrary arity and supports multi-task learning, enhancing accuracy through shared representations across related tasks.

Graph LSTMs enable the integration of previous approaches, including chain or tree LSTMs, by accommodating a broader range of linguistic analyses via graph-based dependencies. This flexibility offers a unified method to explore different LSTM methodologies and enhances the model's ability to handle complex relational patterns.

Results and Evaluation

The proposed framework was evaluated in two precision medicine domains, demonstrating its applicability in both conventional supervised learning and distant supervision contexts. The experimental results revealed that cross-sentence extraction significantly augmented the constructed knowledge bases. Multi-task learning contributed to improved extraction accuracy by leveraging shared structures among related relations.

One of the key findings was the marked improvement in extraction yield, with cross-sentence extraction tripling the amount of extracted knowledge compared to single-sentence approaches. The manual evaluation confirmed the high accuracy of the extracted relations despite the lack of annotations.

Implications and Future Directions

This framework holds considerable implications for domains requiring detailed relational insights, particularly biomedical fields where interpreting complex interactions is crucial. By enabling cross-sentence extraction and supporting multi-task learning, the model reduces the dependency on extensive annotated datasets and makes full use of available linguistic information.

Future research could focus on optimizing coreference and discourse relation modeling within the Graph LSTM framework to further enhance performance. Another avenue of exploration could involve the application of this framework to other domains, thus extending its utility and refining its generalizability.

Overall, the paper contributes significantly to the field by expanding the potential for relational knowledge extraction across sentence boundaries, offering a robust approach for handling complex $n$-ary relations in rich textual domains.