Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions

Nazaruka, Ērika; Osis, Jānis; Griberman, Viktorija

doi:10.1007/978-3-030-40223-5_1

Cited by 1 publication

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent advancements in data-driven machine-and deep-learning methods have increased interest in research into grammatical dependency. Several libraries and tools are available to facilitate parsing dependencies, including spaCy, Natural Language Toolkit (NLTK) [42] and Stanford CoreNLP [43]. Stanford CoreNLP offers a comprehensive and robust combination of rule-based and statistical NLP modelling pipelines used for a range of biological text dependency parsing tasks [44].…”

Section: Dependency Parsingmentioning

confidence: 99%

“…Hence, harnessing the capability to handle complex linguistic structures afforded by a transformer serving as the foundation of the pipeline, and considering its successful applications for tasks in other domains, we utilised spaCy's en_core_web_trf parser dependency pipeline in this study. Several libraries and tools are available to facilitate parsing dependencies, including spaCy, Natural Language Toolkit (NLTK) [42] and Stanford CoreNLP [43]. Stanford CoreNLP offers a comprehensive and robust combination of rule-based and statistical NLP modelling pipelines used for a range of biological text dependency parsing tasks [44].…”

Section: Dependency Parsingmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge-Based Intelligent Text Simplification for Biological Relation Extraction

Gill,

Chetty,

Lim

et al. 2023

Informatics

View full text Add to dashboard Cite

Relation extraction from biological publications plays a pivotal role in accelerating scientific discovery and advancing medical research. While vast amounts of this knowledge is stored within the published literature, extracting it manually from this continually growing volume of documents is becoming increasingly arduous. Recently, attention has been focused towards automatically extracting such knowledge using pre-trained Large Language Models (LLM) and deep-learning algorithms for automated relation extraction. However, the complex syntactic structure of biological sentences, with nested entities and domain-specific terminology, and insufficient annotated training corpora, poses major challenges in accurately capturing entity relationships from the unstructured data. To address these issues, in this paper, we propose a Knowledge-based Intelligent Text Simplification (KITS) approach focused on the accurate extraction of biological relations. KITS is able to precisely and accurately capture the relational context among various binary relations within the sentence, alongside preventing any potential changes in meaning for those sentences being simplified by KITS. The experiments show that the proposed technique, using well-known performance metrics, resulted in a 21% increase in precision, with only 25% of sentences simplified in the Learning Language in Logic (LLL) dataset. Combining the proposed method with BioBERT, the popular pre-trained LLM was able to outperform other state-of-the-art methods.

show abstract

Section: Dependency Parsingmentioning

confidence: 99%

Section: Dependency Parsingmentioning

confidence: 99%