CONNER : A Cascade Count and Measurement Extraction Tool for Scientific Discourse (2021)

Abstract: This paper presents our wining contribution to SemEval 2021 Task 8: MeasEval. The purpose of this task is identifying the counts and measurements from clinical scientific discourse, including quantities, entities, properties, qualifiers, units, modifiers, and their mutual relations. This task can be induced to a joint entity and relation extraction problem. Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model. We provide a detailed description of the proposed model hereinafter. Furthermore, the impact of the essential modules and our in-process technical schemes are also investigated.

## introduction clinicians are currently coping with a massive amount of information, both from raw experimental data and scientific publications recording their results. however, the ever-expanding information sources have exceeded the ability of clinicians to digest and utilize them properly @xcite @xcite . clinical information extraction tools @xcite @xcite @xcite in the text-mining field make an effort to alleviate the clinician's burden according to exploit how to better utilize the knowledge contained in scientific discourse, accessible in the form of natural human language. automating the process of understanding the relevant parts of the scientific literature allows for effective searching, and enabling inference of new information and hypothesis generation for clinical research. counts and measurements are an important part of information source from the scientific discourse @xcite . however, extracting these count and measurement entities and their interactions is challenging, since the way scientists write them can be ambiguous and inconsistent, and the location of this information relative to the measurement can vary greatly. in this paper, we focus on the semeval 2021 measeval task which is composed of five sub-tasks that cover span extraction, classification, and relation extraction. as shown in figure 1 it firstly demands to identify all the quantity spans given a paragraph from a scientific text. for each identified quantity, we need to extract the measured entity, measured property and qualifier which are corresponded to identified quantity. besides, the relationships between quantity, measured entity, measured property and qualifier are also required to be identified. lastly, the unit and type of the quantity is also needed to be recognized if they exist. however, some elements are only present for the largest bed inventory of 13 kg e.g., ti, cr, and mn. however, all elements included within fig. 5 can be considered to be at very low concentrations @xmath0. ## conclusion we proposed conner, a cascade count and measurement extraction tool to jointly identify the quantities and their attached items, as well as the corresponding relations for semeval 2021 task 8: mea-seval. our model extracts these entities and relations in a two-step pipeline method. we also exploited various of technical schemes during the competition and select the one that gains the best performance in the experiments, which help us win second-place in the final ranking.