Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched A bstract M eaning R epresentation (2021)

Abstract: Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model’s understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledge-enriched AMR graph for biomedical IE tasks. Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community.

## introduction the task of biomedical information extraction (ie) aims to extract structured knowledge from biomedical literature, which is usually represented by an information network composed of scientific named 1 data and source code are publicly available at https: //github.com/zhangzx-uiuc/knowledge-amr . ie from biomedical scientific papers presents two unique and non-trivial challenges. first, the authors of scientific papers tend to compose long sentences, where the event triggers and entity mentions are usually located far away from each other within the sentence. as shown in dataset average distance maximal distance ace05-e 0.212 sentence 56 words genia-2011 0.330 sentence 77 words moreover, comprehending sentences from scientific papers urgently requires external knowledge, because there are a number of domain-specific un-we identified a cell-type-specific differential response: creb, ctf, otf-1, oft-2, and nf-kappa b genes were strongly induced 1 to 4 hours after influenza a virus infection in the monocytic cell line mono mac 6, while in freshly prepared human monocytes no significant changes were detected. explained common expressions, acronyms, and abbreviations that are difficult for the model to understand. for instance, as shown in figure 1 , it is nearly impossible for a typical end-to-end model, which only takes in the sentence as input, to get clear understanding of ctf, otf-1, and otf-2 without background knowledge. moreover, the complex biomedical and chemical interactions between multifarious chemicals, genes, and proteins are even harder to understand in addition to the entities themselves. to tackle these two challenges, we propose a novel framework for biomedical ie that integrates abstract meaning representation (amr) @xcite and external knowledge graphs. amr is a semantic representation language that converts the meaning of each input sentence into a rooted, directed, labeled, acyclic graph structure. amr semantic representation includes propbank @xcite frames, non-core semantic roles, coreference, entity typing and linking, modality, and negation. the nodes in amr are concepts instead of words, and the edge types are much more fine-grained compared with traditional semantic languages like dependency parsing and semantic role labeling. we train a transformer-based amr semantic parser @xcite on biomedical scientific texts and use it in our biomedical ie model. to better handle long sentences with distant trigger and entity pairs, we use amr parsing to compress each sentence and to better capture global interactions between tokens. for example, as shown in figure 1 , the positive regulation event trigger "changes" is located far away from its arguments ctf, otf-1, otf-2 in the original sentence. however, in the amr graph, such trigger-entity pairs are linked within two hops. therefore, it will be much easier for the model to identify such kinds of events with the guidance of amr parsing. in addition, to make better use of the external knowledge, we extract a global knowledge graph from the comparative toxicogenomics database (ctdb) that covers all biomedical entities in the corpus. for each sentence, we select a minimal connected subgraph as the sentence-level kg. we use this sentence kg to enrich amr nodes and edges to give the model additional prior domain knowledge, especially the biomedical and chemical interactions between different genes and proteins. these fine-grained relations are important for biomedical event extraction. for example, as in figure 1 , the incorporation of the external kg can indicate that mono mac 6 can result in leukemia, which will affect the expression of ctf, otf-1, and oft-2 proteins. with this external knowledge, it will be much easier for the model to identify such proteins as the arguments of a positive regulation event. we encode the knowledge-enriched amr graph using an edge-conditioned graph attention network (gat) that is able to incorporate finegrained edge features before conducting ie tasks. we evaluate our model on the existing benchmark genia-2011 dataset where our model greatly outperforms our baseline model by 4.8%. in addition to the existing genia-2011 benchmark, we also aim to evaluate the effectiveness of our framework on topic-specific literature. we develop a new ontology for entities and events with a large corpus from covid-19 research papers, which is specifically annotated by medical professionals and can serve as a new benchmark for the biomedical ie community. the major contributions of this paper are summarized as follows. ## related work biomedical information extraction a number of previous studies contribute to biomedical event extraction with various techniques, such as dependency parsing @xcite , external knowledge base @xcite , joint inference of triggers and arguments @xcite , abstract meaning representation @xcite , search based neural models @xcite , and multi-turn question answering @xcite . recently, to handle the nested biomedical events, beesl @xcite models biomedical event extraction as a unified sequence labeling problem for end-to-end training. deepeventmine @xcite proposes to use a neural network based classifier to decide the structure of complex nested events. our model is also in an end-to-end training pipeline, but additionally utilizes fine-grained amr semantic parsing and external knowledge to improve the performance. ## conclusions and future work in this paper, we propose a novel biomedical information extraction framework to effectively tackle two unique challenges for scientific domain ie: complex sentence structure and unexplained concepts. we utilize amr parsing to compress wide contexts, and incorporate external knowledge into the amr. our proposed model produces significant performance gains compared with most stateof-the-art methods. in the future, we intend to exploit tables and figures in the scientific literature for multimedia representation. we also plan to further incorporate coreference graphs among sentences to further enrich contexts. we will also continue exploring the use of richer information from an external knowledge base to further improve the model's performance.