Biomedical information is growing rapidly in the recent years and retrieving useful data through information extraction system is getting more attention. In the current research, we focus on different aspects of relation extraction techniques in biomedical domain and briefly describe the state-of-the-art for relation extraction between a variety of biological elements.

Abstract Few-shot relation extraction is one of the current research focuses. The key to this fully extract semantic information through very little training data. Intuitively, raising semantics awareness in sentences can improve efficiency model features alleviate overfitting problem few-shot learning. Therefore, we propose an enhanced feature based on prototype network relations from texts. F...

The main task of Ontology learning is concept extraction and conceptual relation extraction. This paper mainly studies the latter. Conceptual relation consists of taxonomic relation and non-taxonomic relation. It introduces hierarchy clustering method, and uses concept hierarchy clustering method which chooses different clustering standards in each hierarchy to obtain the taxonomic relation. It...

Abstract. This paper presents the extension of an existing mimimally supervised rule acquisition method for relation extraction by coreference resolution (CR). To this end, a novel approach to CR was designed and tested. In comparison to state-of-the-art methods for CR, our strategy is driven by the target semantic relation and utilizes domain-specific ontological and lexical knowledge in addit...

In this thesis, we study the importance of background knowledge in relation extraction systems. We not only demonstrate the benefits of leveraging background knowledge to improve the systems’ performance but also propose a principled framework that allows one to effectively incorporate knowledge into statistical machine learning models for relation extraction. Our work is motivated by the fact ...

Extracting Lives In relations between bacteria and their locations involves two steps, namely bacteria/location entity recognition and Lives In relation classification. Previous work solved this task by pipeline models, which may suffer error propagation and cannot utilize the interactions between these steps. We follow the line of work using joint models, which perform two subtasks simultaneou...

Relation extraction is the task of extracting predicate-argument relationships between entities from natural language text. This paper investigates whether background information about entities available in knowledge bases such as FreeBase can be used to improve the accuracy of a state-of-the-art relation extraction system. We describe a simple and effective way of incorporating FreeBase’s nota...

Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHESSION, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. ...

Relation extraction is a fundamental task in information extraction. Different methods have been studied for building a relation extraction system. Supervised training of models for this task has yielded good performance, but at substantial cost for the annotation of large training corpora (About 40K same-sentence entity pairs). Semi-supervised methods can only require a seed set, but the perfo...

Part-whole relation, ormeronymy plays an important role in many domains. Among approaches to addressing the part-whole relation extraction task, the Espresso bootstrapping algorithm has proved to be effective by significantly improving recall while keeping high precision. In this paper, we first investigate the effect of using fine-grained subtypes and careful seed selection step on the perform...