Method Entity Extraction from Biomedical Texts (2022)

Abstract: In the field of Natural Language Processing (NLP), extracting method entities from biomedical text has been a challenging task. Scientific research papers commonly consist of complex keywords and domain-specific terminologies, and new terminologies are continuously appearing. In this research, we find method terminologies in biomedical text using both rule-based and machine learning techniques. We first use linguistic features to extract method sentence candidates from a large corpus of biomedical text. Then, we construct a silver standard biomedical corpus composed of these sentences. With a rule-based method that makes use of the Stanza dependency parsing module, we label the method entities in these sentences. Using this silver standard corpus we train two machine learning algorithms to automatically extract method entities from biomedical text. Our results show that it is possible to develop machine learning models that can automatically extract method entities to a reasonable accuracy without the need for a gold standard dataset.

## introduction method entity extraction from unstructured biomedical text has been an important, yet challenging and somewhat under-examined, natural language processing (nlp) task. especially in the field of biomedicine, the automatic extraction of methodology names and terminologies is imperative. with thousands of research papers published each week, the biomedical research community is constantly creating new terminologies. as a result, it becomes difficult for researchers to find relevant information. @xcite give a good discussion of method entities and provide the following definition: "named entities that represent specific methods". more specifically, "method entities in the academic literature are nouns or noun phrases representing the specific ways, means, and channels used to solve tasks or problems proposed by the authors, including sub-categories such as disciplinespecific methods, software, models, algorithms, and metrics". biomedical method entities are a type of biomedical named entities. biomedical named entity recognition (ner) is defined as the task of recognizing and categorizing entity names in the biomedical domain @xcite . as mentioned by @xcite , biomedical ner faces difficulties for various reasons. the first one is the increasing rate of newly created terminologies and keywords requiring new rules and patterns to be manually added to the rule-based methods, which can be a tedious and time-consuming task. regarding biomedical method entity recognition, because it has been little studied (e.g., @xcite ), a comprehensive definition of what constitutes a biomedical method entity beyond that given by @xcite still needs to be determined. secondly, with information extraction tasks, the same words can have different meanings and significance in terms of the context. in this study, our main contribution is the exploration of a rule-based approach to create a silver standard corpus annotated for method entities foot_0 . regarding the rule-based approach, rules to extract method entities based on patterns of universal dependency relations (universal dependencies, 2014) between words will be designed. to evaluate the machine-made silver standard corpus, this corpus will be used as training data for two machine learning models, conditional random fields (crf) @xcite and bilstm @xcite . crf was used by @xcite and our crf results are compared to their results as a baseline. the bilstm results indicate that a larger corpus will be needed for neural learning models. the structure of the remainder of this paper is as follows. in section 2, we will review the background and related work. in section 3, we will provide our research contributions. we will review the methodology of our research in section 4. in section 5, we will review the results. and in section 6, we will conclude the paper and suggest directions for the continuation of this study. ## background and related work named entity recognition (ner) is an application of natural language processing (nlp) where entities are tagged according to various semantic and syntactic rules. surveys of research in the field of ner @xcite indicate that automatic ner extraction has received significant coverage in the past three decades. for the study reported in the current paper, we are interested in the extraction of a subcategory of named entities, method entities in particular, from biomedical literature. in the biomedical literature, extraction of named entities has tended to focus on biological entities (for example, @xcite and @xcite ). extraction of method entities, in particular, has received some attention, but the vast majority extract this type of named entity from non-biomedical literature @xcite . one exception is @xcite who deal with method entities in biomedical research articles. @xcite extract methodology terms as well as sleep disorder entities from biomedical literature that focusses on sleep disorders. @xcite propose a new annotation scheme, manually annotate 3,088 resource citations (algorithms are the only methods included in these resources) found in biomedical and non-biomedical literature, and use bilstm as the machine learning method. one aspect of this current work is to create a silver standard corpus for method entities in biomedical text. the automatic method to generate this corpus will use stanza's dependency parser @xcite with the biomedical packages @xcite . this parser produces graph structures whose edges are labelled with universal dependencies (universal dependencies, 2014). to evaluate using the silver standard corpus, we will use conditional random fields @xcite and bilstm @xcite , the machine learning techniques used in other studies @xcite . ## research contributions few researchers have addressed the question of automatic method entity extraction from biomedical text. generating a human-labelled corpus is time-consuming. this study aims to address these issues. stanza's universal dependencies while extracting a wider variety of method entities compared to @xcite and have successfully created an accurate silver standard corpus (precision score of 97.59) prepared from full text biomedical articles selected from the pubmed central dataset with method entities annotated automatically. because @xcite is the only previous study that investigates the same problem, we use that work as the baseline and compare our results with those reported there. in the initial stage of the study, we prepare a collection of sentences that contain mentions of method names, or "method sentences", using the method proposed by @xcite . by employing the properties of anaphoric relations between sentences, we can collect the "method sentences" in a convenient and feasible manner. we collect these sentences by scanning through research papers using the unix command grep and selecting some number of sentences that precede any sentence containing the words "this method". this approach successfully generates a corpus containing solely "method sentences". after the corpus creation has been completed, the next stage involves utilizing linguistic rules and patterns to automatically label method entities. in this study, for tagging the entities, we will be using the iob tagging format. in the iob tagging scheme, every token is labelled as b-label if the token is the beginning of a named entity, i-label if it is inside a named entity but not the first token within the named entity, or o otherwise @xcite . leveraging rule-based methods allows for labelling the method mentions without the use of any pre-existing training data; additionally, a secondary benefit of this approach is the potential of introducing new rules and patterns based on the linguistic features of the terminologies. this step is an essential aspect of our research as it allows for implementing an accurate silver standard dataset. after the rule-based methods have been applied, traditional and neural learning techniques can be explored in combination with the newly developed silver standard dataset. the primary benefit of utilizing a machine learning approach is the ability to generalize beyond the limits of the rules and patterns that are manually defined in the rulebased approach. in this stage of the research, we will explore two machine learning algorithms related to natural language processing tasks: conditional random field (crf) models @xcite and the bidirectional long short term memory (bilstm) models @xcite . we opted for choosing these algorithms as crf models are trained for sequence segmentation and labelling and bilstm models have been used in other named entity extraction research @xcite . ## conclusions and future work in this paper, we explored various methodologies to automatically extract method entities from biomedical text. in the initial step, we created a corpus containing candidate method sentences using anaphoric relations. next, we investigated a rulebased method using information provided by a dependency parse to iob tag the method entities in the corpus. this silver standard corpus is the main contribution of our work and has been made publicly available. to evaluate the quality of this corpus, we trained two machine learning methods using this silver standard corpus to automatically extract method entities from biomedical text. the evidence from this study shows that using a dependency parser that is pre-trained on biomedical vocabulary allows for precise extraction of method entities within the scope of the rules as shown in section 5.1. additionally, the results from section 5.2 and our future work will include 1. improving on the anaphoric method to gather candidate method entity sentences. the bilstm results strongly suggest that the corpus is too small for neural learning methods.