Biomedical Event Extraction using Abstract Meaning Representation

Rao, Sudha; Marcu, Daniel; Knight, Kevin; Daumé, Hal

doi:10.18653/v1/w17-2315

Cited by 58 publications

(50 citation statements)

References 24 publications

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“…Intuitively, heuristic rules can be applied as a step of pre-processing or post-processing to merge multiple entities into one for relation classification. The machine learning-based solution might be to use Abstract Meaning Representation (AMR) (52) to trim the sentence and use the structured sentence abstract, which also removes the redundancy in the sentences and proved to be effective for other biomedical relation extraction tasks (15, 53). The semantic embeddings of AMR and dependency parsing results can be used as other word-level embeddings, such as word embeddings and position embeddings in this study.…”

Section: Discussionmentioning

confidence: 99%

Extracting chemical–protein relations using attention-based neural networks

et al. 2018

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Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical–protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical–protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering. The source code of this work is available at https://github.com/ohnlp/att-chemprot.

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Section: Discussionmentioning

confidence: 99%

Extracting chemical–protein relations using attention-based neural networks

et al. 2018

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“…We evaluate our results on the test set using the official online tool provided by the Genia task organizers. 1 Following previous studies (Björne and Salakoski, 2011;Venugopal et al, 2014;Rao et al, 2017;Björne and Salakoski, 2018), we report scores obtained by the approximate span (allowing trigger spans to differ from gold spans by single words). As we only focus on matching core arguments, we use recursive matching criterion for evaluation which not requires matching of additional arguments for events referred from other events (Kim et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

Biomedical Event Extraction based on Knowledge-driven Tree-

Huang

2019

Proceedings of the 2019 Conference of the North

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Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts. To better encode contextual information and external background knowledge, we propose a novel knowledge base (KB)-driven treestructured long short-term memory networks (Tree-LSTM) framework, incorporating two new types of features: (1) dependency structures to capture wide contexts; (2) entity properties (types and category descriptions) from external ontologies via entity linking. We evaluate our approach on the BioNLP shared task with Genia dataset and achieve a new stateof-the-art result. In addition, both quantitative and qualitative studies demonstrate the advancement of the Tree-LSTM and the external knowledge representation for biomedical event extraction.

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“…AMR mainly focuses on content words, some surface words such as function words ("to", "the") which do not contribute to the meaning of a sentence, will be omitted from a sentence. Recently, AMR has been utilized as some NLP tasks, such as entity linking [30], event extraction [31], [32], text summarization [33]- [36], machine translation [37].…”

Section: B Semantic Structure Based Approaches For Nlp Tasksmentioning

confidence: 99%

“…Rao et al [32] used a deep semantic representation based on AMR to extract events in biomedical text and hypothesized that an event is an AMR subgraph. Based on the assumption, event extraction task is considered as a graph identification problem.…”

Section: B Semantic Structure Based Approaches For Nlp Tasksmentioning

confidence: 99%

Deep2s: Improving Aspect Extraction in Opinion Mining With Deep Semantic Representation

et al. 2020

IEEE Access

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Syntactical rule based approaches for aspect extraction, which are free from expensive manual annotation, are promising in practice. These approaches extract aspects mainly through the dependency relations in the surface sentence structures. However, deep and rich semantic information hidden in sentences which can help improve aspect extraction, is difficult for them to capture. In order to address the problem, this paper first proposes to employ Logic Programming to explore the feasibility of deep semantic representation, then proposes Deep2S, a hybrid rule-based method to improve the performance of aspect extraction. Deep2S integrates deep semantic representation such as Abstract Meaning Representation (AMR) with syntactic structure. It can take advantage of the syntactical rules to obtain dependency relations in the surface structure as well as the semantic rules to capture deep semantic information. Our experiments are conducted on eight popular review datasets using two evaluation metrics. Experimental results demonstrate the usefulness of deep semantic representation and the ability of Deep2S to improve the performance of aspect extraction in opinion mining.

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Biomedical Event Extraction using Abstract Meaning Representation

Cited by 58 publications

References 24 publications

Extracting chemical–protein relations using attention-based neural networks

Extracting chemical–protein relations using attention-based neural networks

Biomedical Event Extraction based on Knowledge-driven Tree-

Deep2s: Improving Aspect Extraction in Opinion Mining With Deep Semantic Representation

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