Natural Language System for Terminological Information Retrieval

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

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Integrating natural language processing and information retrieval in a troubleshooting help desk

IEEE Expert ◽

10.1109/64.248348 ◽

1993 ◽

Vol 8 (6) ◽

pp. 9-17 ◽

Cited By ~ 5

Author(s):

P.G. Anick

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Help Desk

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Applying Natural Language Processing, Information Retrieval and Machine Learning to Decision Support in Medical Coordination in an Emergency Medicine Context

2015 IEEE 28th International Symposium on Computer-Based Medical Systems ◽

10.1109/cbms.2015.82 ◽

2015 ◽

Cited By ~ 2

Author(s):

Juliana Tarossi Pollettini ◽

Hugo Cesar Pessotti ◽

Antonio Pazin Filho ◽

Evandro Eduardo Seron Ruiz ◽

Mario Sergio Adolfi Junior

Keyword(s):

Machine Learning ◽

Emergency Medicine ◽

Information Retrieval ◽

Natural Language Processing ◽

Decision Support ◽

Natural Language ◽

Language Processing ◽

Processing Information

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Information retrieval using a transportable natural language interface

ACM SIGIR Forum ◽

10.1145/1013230.511804 ◽

1983 ◽

Vol 17 (4) ◽

pp. 81-86 ◽

Cited By ~ 1

Author(s):

Madeleine Bates ◽

Robert J. Bobrow

Keyword(s):

Information Retrieval ◽

Natural Language ◽

Natural Language Interface

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Review: hindi question answering system using machine learning approach As an upshot of Natural Language Interface to Database (NLIDB), Question Answering System is relatively an Information Retrieval system which is suppose to reflect the user with the correct or closest results to the query being asked to the system in natural language. Information Retrieval and Information Extraction plays vital role in accomplishing the task of interaction between the user and the system. The paper discusses various ways and techniques of interaction between the user and the system along with different approaches. Machine Learning is one of the approaches which is preferred for the Question Answering System. Out of Supervised and unsupervised learning, supervised learning is taken priority here by going through numerous other techniques

International Journal of Latest Trends in Engineering and Technology ◽

10.21172/1.74.035 ◽

2016 ◽

Vol 7 (4) ◽

Keyword(s):

Machine Learning ◽

Information Retrieval ◽

Natural Language ◽

Question Answering ◽

Retrieval System ◽

Vital Role ◽

Learning Approach ◽

Question Answering System ◽

Natural Language Interface ◽

Machine Learning Approach

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Information retrieval using robust natural language processing

Proceedings of the workshop on Speech and Natural Language - HLT '91 ◽

10.3115/1075527.1075573 ◽

1992 ◽

Author(s):

Tomek Strzalkowski

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing

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Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation (Preprint)

10.2196/preprints.17376 ◽

2019 ◽

Cited By ~ 1

Author(s):

Sijia Liu ◽

Yanshan Wang ◽

Andrew Wen ◽

Liwei Wang ◽

Na Hong ◽

...

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Data Model ◽

Structured Data ◽

Common Data Model ◽

Concept System ◽

Unstructured Text ◽

Electronic Health

BACKGROUND Widespread adoption of electronic health records has enabled the secondary use of electronic health record data for clinical research and health care delivery. Natural language processing techniques have shown promise in their capability to extract the information embedded in unstructured clinical data, and information retrieval techniques provide flexible and scalable solutions that can augment natural language processing systems for retrieving and ranking relevant records. OBJECTIVE In this paper, we present the implementation of a cohort retrieval system that can execute textual cohort selection queries on both structured data and unstructured text—Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records (CREATE). METHODS CREATE is a proof-of-concept system that leverages a combination of structured queries and information retrieval techniques on natural language processing results to improve cohort retrieval performance using the Observational Medical Outcomes Partnership Common Data Model to enhance model portability. The natural language processing component was used to extract common data model concepts from textual queries. We designed a hierarchical index to support the common data model concept search utilizing information retrieval techniques and frameworks. RESULTS Our case study on 5 cohort identification queries, evaluated using the precision at 5 information retrieval metric at both the patient-level and document-level, demonstrates that CREATE achieves a mean precision at 5 of 0.90, which outperforms systems using only structured data or only unstructured text with mean precision at 5 values of 0.54 and 0.74, respectively. CONCLUSIONS The implementation and evaluation of Mayo Clinic Biobank data demonstrated that CREATE outperforms cohort retrieval systems that only use one of either structured data or unstructured text in complex textual cohort queries.

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