Automating the generation of lexical patterns for processing free text in clinical documents

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Bioinformatics (methodology)

Background:

• Lexical pattern-matching is crucial for NLP tasks like information extraction (IE).
• Achieving both high recall and precision in pattern generation is challenging.
• Existing methods often struggle with consistency across diverse tasks.

Purpose of the Study:

• To introduce a multiple sequence alignment (MSA)-based technique for automated lexical pattern generation.
• To leverage linguistic context and identify stable patterns for improved NLP performance.
• To enhance recall and precision in information extraction tasks.

Main Methods:

• Utilized multiple sequence alignment (MSA) to analyze word sequences and identify commonalities.
• Developed a framework that automatically generates generalizable lexical patterns.
• Applied the MSA-based technique to four different information extraction tasks.

Main Results:

• MSA-generated patterns demonstrated consistent F1, F.5, and F2 scores across all four tasks.
• Outperformed two baseline techniques in terms of consistent high-level performance.
• Showcased versatility in handling both single data elements and relations between concepts.

Conclusions:

• The MSA-based framework offers a versatile solution for lexical pattern generation in NLP.
• The method effectively balances high performance and recall without extensive manual input.
• MSA provides a systemic approach to creating robust and generalizable linguistic patterns.