Entity Coreference and Co-occurrence Aware Argument Mining from Biomedical Literature (2023)

Abstract: Biomedical argument mining (BAM) aims at automatically identifying the argumentative structure in biomedical texts. However, identifying and classifying argumentative relations (AR) between argumentative components (AC) is challenging since it not only needs to understand the semantics of ACs but also need to capture the interactions between them. We argue that entities can serve as bridges that connect different ACs since entities and their mentions convey significant semantic information in biomedical argumentation. For example, it is common that related AC pairs share a common entity. Capturing such entity information can be beneficial for the Relation Identification (RI) task. In order to incorporate this entity information into BAM, we propose an Entity Coreference and Co-occurrence aware Argument Mining (ECCAM) framework based on an edge-oriented graph model for BAM. We evaluate our model on a benchmark dataset and from the experimental results we find that our method improves upon state-of-the-art methods.

## introduction there is a growing interest in evidence-based decision making in the biomedical field, as it can assist medical practitioners in selecting the best treatment for a given medical case. however, extracting relevant evidence from vast amounts of biomedical publications is time-consuming for practitioners. thus, biomedical argument mining (bam), which is the application of argument mining (am) to biomedical texts, is proposed to automatically extract argumentative structures in biomedical texts by identifying argument components (ac) and argument relations (ar) between acs @xcite . bam includes three primary tasks @xcite : (1) argument component identification (aci)-i.e., distinguishing argumentative components from non-argumentative content; (2) argument component classification (acc)-categorizing acs into different types (e.g., claim, and evidence); and (3) relation identification (ri)-recognizing ars (e.g., support, attack, or none) between a pair of acs. among these tasks, the ri task is the hardest one and existing models tend to underperform on this task, compared to the aci and acc tasks @xcite @xcite . one possible reason is that these models do not incorporate the information about the co-occurrence of common entities between different acs. this is a valuable source of semantic information and can be particularly important in bam. as shown in fig 1 , the ac pairs connected by an ar share entities with coreference relations. furthermore, entity co-occurrence suggests the direction of the ars (i.e. acs with several entities are usually the tail of ars, like in figure 1 ). based on this intuition, we propose an entity coreference and co-occurrence aware argument mining (eccam) framework that effectively captures ars through entity coreference and cooccurrence. eccam is a graph-based model. we build a heterogeneous graph that consists of nodes that represent acs and entities, and edges between nodes. the entity nodes can serve as bridges that connect different acs. considering that the entity coreference and co-occurrence relations exist between nodes and thus are represented by edge embeddings, we employ an edge-oriented graph model @xcite that learns edge representations of any two connected nodes by combing all paths between the two nodes. this enables information flow between different relations and iteratively updates the edge representations, which is finally used as the representations of ars. here, the edges between ac nodes and entity nodes are used to pass the entity coreference information while the edges between entity nodes aim to leverage entity co-occurrence information. figure 1 : part of the argument structure of the abstracts from pubmed 23589316. each text in the rectangle represents an ac. two acs connected with an arrow means that there is an ar between them. entities with the same colour are in the same coreference clusters. our contributions are shown below: ## related work recently, the research community has shown growing interest in the task of bam. @xcite created a dataset by annotating acs within randomized controlled trial abstracts and employ the sub-set tree kernel to classify the types of acs with bag-of-words of biomedical text as input. further, a dataset with both acs and ars are created by @xcite to deal with three tasks of bam: aci, acc and ri. various contextualized word embeddings, such as bert @xcite , biobert @xcite , scibert @xcite , and roberta @xcite are explored to address these tasks. @xcite incorporate zoning information (such as background, result and conclusion) to tackle the aci and acc tasks at the same time. @xcite employed a multi-task framework with an attentive residual network to address the acc task, ri task, and link prediction task of bam, based on the assumption that acs had been detected. seqmt @xcite also assumes the aci task is solved and pays attention to the acc and ri tasks. it utilises a multi-task learning approach to benefit from the sequential dependency between the acc and ri tasks by transferring the representation of the input and output of the acc task to the ri task. however, none of the previous models leverages the entity coreference and co-occurence information for the ri task, which is the focus of this paper. ## model architecture following previous models @xcite @xcite , we assume that the outputs of the aci task are provided, i.e., all acs have been detected without ac types. inspired by @xcite who employ an edgeoriented graph model @xcite to leverage interactions among sentences that share the same entities for document-level relation extraction, we propose a framework for the ri task based on a similar edge-oriented model. our framework contains three parts: the entity cluster extraction module, the document encoder module and the entity co-occurrence and coreference-aware argument mining module. ## conclusion in this paper, we propose the eccam model that is based on an edge-oriented graph model @xcite to incorporate entity coreference and co-occurrence information into bam. we introduce edges between entity nodes and ac nodes in a heterogeneous graph to help our model capture entity coreference and co-occurrence information respectively. experiments on the abstrct dataset show the effectiveness of these two types of information for the ri task. in the future, we will apply our method to other argument mining domains, such as student essays @xcite .