In SRL, sentences are represented by one or more predicate-argument structures ( PAS), also known as propositions. Semantic role labeling ( SRL), also called shallow semantic parsing, is a popular semantic analysis technique for extracting relations. A key IE task in this field is the extraction of relations between named entities (NEs), such as protein-protein and gene-disease interactions. Many information extraction (IE) systems incorporating natural language processing (NLP) techniques have been developed for use in the biomedical field. The amount of biomedical literature available online continues to grow rapidly today, creating a need for automatic processing using bioinformatics tools. Our system can match the performance of other state-of-the-art domain-specific ML-based SRL systems and can be easily customized for PASBio application development. Our approach allows PAS conversion between BioProp and PASBio annotation using BIOSMILE alongside our newly developed semi-automatic rule generator and rule-based converter. The system achieved an F-score of 69.08% for PASBio's 29 verbs. The second experiment evaluated combined system (BIOSMILE + rule-based converter). The converter achieved an F-score of 85.29%. Our first experiment evaluated our rule-based converter's performance independently from BIOSMILE performance. Our system consists of BIOSMILE in combination with a BioProp-PASBio rule-based converter, and an additional semi-automatic rule generator. In this paper, we aim to build a system to convert BIOSMILE's BioProp annotation output to PASBio annotation. In previous work, we constructed a biomedical corpus based on the PropBank standard called BioProp, on which we developed an ML-based SRL system, BIOSMILE. Unfortunately, due to the lack of an annotated PASBio corpus, no publicly available machine-learning (ML) based SRL systems based on PASBio have been developed. In the biomedical field, however, more detailed and restrictive PAS annotation formats such as PASBio are popular. PropBank is the most widely used PAS corpus and annotation format in the newswire domain. Each PAS is composed of a predicate (verb) and several arguments (noun phrases, adverbial phrases, etc.) with different semantic roles, including main arguments (agent or patient) as well as adjunct arguments (time, manner, or location). In SRL, sentences are represented by one or more predicate-argument structures (PAS). Semantic role labeling (SRL) is an important text analysis technique.
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