Syntactic methods rely on textual features extracted from varied syntactic structures like coreference annotations, dependency parsing trees, and discourse relations, to connect sentences in documents.


Syntactic methods for RE from complicated context 

Inter-sentence entity graphs can use multi-hop paths between entities for inferring the correct relations. 


Multi-hop paths between entities for RE from complicated context


Graph sentence neural networks can be employed to model cross-sentence dependencies for relation extraction, which bring in memory and reasoning abilities


Graph-structured neural networks for RE from complicated context

- Syntactic methods
- Multi-hop paths between entities
- Graph-structured neural networks

Columbia University

In Relation Extraction (RE), a single document often mentions multiple entities exhibiting complex cross-sentence relationships. Most existing RE methods focus on intra-sentence extraction and are inadequate for collectively identifying relational facts expressed across long paragraphs. Extracting these facts requires models capable of handling complicated contexts, such as full documents, rather than relying solely on isolated single sentences.

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