Semantic Similarity Based Approach for Automatic Evaluation of Free Text Answers Using Link Grammar

2014 ◽  
Author(s):  
Udit Kr. Chakraborty ◽  
Rashmi Gurung ◽  
Samir Roy
Keyword(s):  
Semantic Similarity ◽  
Free Text ◽  
Automatic Evaluation ◽  
Link Grammar

A Fuzzy Indiscernibility Based Measure of Distance between Semantic Spaces Towards Automatic Evaluation of Free Text Answers

2017 ◽  
Vol 25 (06) ◽  
pp. 987-1004
Author(s):  
Udit Kr. Chakraborty ◽  
Samir Roy ◽  
Sankhayan Choudhury
Keyword(s):  
Semantic Space ◽  
Test Case ◽  
Free Text ◽  
Automatic Evaluation ◽  
Human Language ◽  
Discourse Comprehension ◽  
Language Performance ◽  
Proposed Model ◽  
E Learning ◽  
Semantic Spaces

Quantitative models built as tools for evaluating human language performance, can prove useful for both theoretical and applied areas of discourse comprehension, assessment, and education. The current paper offers a test case focused on mathematical articulation of a model and a test of that model with existing corpora. The model presented has been developed to evaluate free text answers of students based on the fuzzy indiscernibility between the semantic spaces created by the model answer and the learners’ response. The model semantic space represented as a knowledge matrix can be constructed out of one or more model answers prepared by human experts and closely resemble the knowledge of the human evaluator. The proposed model finds out the indiscernibility between the types of word usage and scores the answer based on the fuzzy indiscernibility measures stored in a graded thesaurus prepared specifically for this purpose. The results returned on experimental data correlates well with human evaluators. This simulated learner-friendly atmosphere, inspired by a teacher’s intelligent benevolence, ensures effective attainment of learning objective in e-learning environment.

scholarly journals Inferring Drug-Protein–Side Effect Relationships from Biomedical Text

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 159 ◽  
Author(s):  
Min Song ◽  
Seung Han Baek ◽  
Go Eun Heo ◽  
Jeong-Hoon Lee
Keyword(s):  
Side Effects ◽  
Text Mining ◽  
Semantic Similarity ◽  
Side Effect ◽  
Relation Extraction ◽  
Ranking Function ◽  
Entity Recognition ◽  
Free Text ◽  
Pubmed Database ◽  
Biomedical Texts

Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. Materials and Methods: We extracted three types of relationships—drug-protein, protein-protein, and protein–side effect—from biomedical texts by using text mining and predefined relation-extraction rules. Based on the extracted relationships, we constructed whole drug-protein–side effect paths. For each path, we calculated its ranking score by a new ranking function that combines corpus- and ontology-based semantic similarity as well as co-occurrence frequency. Results: We extracted 13 plausible biomedical paths connecting drugs and their side effects from cancer-related abstracts in the PubMed database. The top 20 paths were examined, and the proposed ranking function outperformed the other methods tested, including co-occurrence, COALS, and UMLS by P@5-P@20. In addition, we confirmed that the paths are novel hypotheses that are worth investigating further. Discussion: The risk of side effects has been an important issue for the US Food and Drug Administration (FDA). However, the causes and mechanisms of such side effects have not been fully elucidated. This study extends previous research on understanding drug side effects by using various techniques such as Named Entity Recognition (NER), Relation Extraction (RE), and semantic similarity. Conclusion: It is not easy to reveal the biomedical mechanisms of side effects due to a huge number of possible paths. However, we automatically generated predictable paths using the proposed approach, which could provide meaningful information to biomedical researchers to generate plausible hypotheses for the understanding of such mechanisms.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Structured Reporting of Medical Findings: Evaluation of a System in Gastroenterology

1992 ◽  
Vol 31 (04) ◽  
pp. 268-274 ◽  
Author(s):  
W. Gaus ◽  
J. G. Wechsler ◽  
P. Janowitz ◽  
J. Tudyka ◽  
W. Kratzer ◽  
...  
Keyword(s):  
Structured Data ◽  
Structured Reporting ◽  
Free Text ◽  
Clinical Documentation ◽  
Abdominal Sonography ◽  
Technical Examination ◽  
Medical Reports ◽  
Time Required ◽  
Upper Abdominal ◽  
Structured Approach

Abstract:A system using structured reporting of findings was developed for the preparation of medical reports and for clinical documentation purposes in upper abdominal sonography, and evaluated in the course of routine use. The evaluation focussed on the following parameters: completeness and correctness of the entered data, the proportion of free text, the validity and objectivity of the documentation, user acceptance, and time required. The completeness in the case of two clinically relevant parameters could be compared with an already existing database containing freely dictated reports. The results confirmed the hypothesis that, for the description of results of a technical examination, structured data reporting is a viable alternative to free-text dictation. For the application evaluated, there is even evidence of the superiority of a structured approach. The system can be put to use in related areas of application.

A General Model of Drug Prescription

1995 ◽  
Vol 34 (04) ◽  
pp. 310-317 ◽  
Author(s):  
B. Séné ◽  
I. de Zegher ◽  
C. Milstein ◽  
S. Errore ◽  
F de Rosis ◽  
...  
Keyword(s):  
User Interface ◽  
Life Events ◽  
Drug Prescription ◽  
Clinical Signs ◽  
Object Oriented ◽  
Drug Prescriptions ◽  
Free Text ◽  
Medical Procedures ◽  
Dosage Regimens ◽  
Structured Representation

Abstract:Currently, there is no widely accepted structured representation of drug prescription. Nevertheless, a structured representation is required for entering and storing drug prescriptions avoiding free text in computerized systems, and for drug prescription reviews. Derived from part of the work of the European OPADE project, we describe an object-oriented model of drug prescription which incorporates important concepts such as the phase and triggering event concepts. This model can be used to record all drug prescriptions, including infusions, in a structured way avoiding free text. The phase concept allows the storage of sequentially ordered dosage regimens for a drug within the same prescription. The prescription triggering event concept allows recording of the administration of a drug conditional to dates, symptoms and clinical signs, medical procedures, and everyday life events. This model has been implemented within the OPADE project; the corresponding aspects of the user interface are presented to show how this model can be used in practice. Even if other new attributes may be added to the described objects, the structure of this model is suitable for general use in software which requires the entry, storage and processing of drug prescriptions.

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