A Proposal of Topic Map Based Chatterbot for Non-English Natural Language Input

Five computerized role-playing scenarios, which accept unrestricted natural language input, were developed and administered to seventy-two freshman medical students. The scenarios, written in CASIP, measured and automatically scored each response on five psychological dimensions: Social skills, level of frustration, submissiveness, combativeness, and negotiative ability. The programmed scenarios also monitored nonverbal dimensions, which may reflect the emotional state of the testee. These included: The time it took to start an answer; the time spent reviewing the answer; the lengths of answers and of the words used. The testees behaved significantly different in handling the different role-playing scenarios. While no significant correlations were found between the psychological dimensions expressed in the different scenarios, the tests identified individual testees who displayed a pattern of extremes of psychological behavior.

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Liana

ITL - International Journal of Applied Linguistics ◽

10.1075/itl.30.01bat ◽

1975 ◽

Vol 30 ◽

pp. 1-24

Author(s):

I. Batoni ◽

R. Henning ◽

H. Lehmann ◽

B. Schirmer ◽

M. Zoeppritz

Keyword(s):

Natural Language ◽

Question Answering ◽

Semantic Analysis ◽

Tree Structure ◽

Semantic Interpretation ◽

Syntactic Analysis ◽

Question Answering System ◽

Continuous Text ◽

Language Input

Abstract LIANA is a question answering system in PL/1. The program takes German natural language input and, by morphological, syntactic and semantic analysis, creates a representation of the text, which is stored and can be accessed for retrieval purposes. All individuals (objects) mentioned in the sentence are found and stored. In continuous text, therefore, information about individuals can be piled up successively. LIANA uses the programming concept of the Boston Syntax Analyzer. Therefore, the output of syntactic analysis is a tree structure, simulated through pointers which connect the nodes in the tree. Each node is associated with a feature table which is operated on by the semantic interpretation. Node and feature handling is facilitated by a set of macros for adding, erasing, and checking features and copying, deleting, and inserting nodes.

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Natural Language Input for Fuzzy Diagnosis

Fuzzy Control ◽

10.1007/978-3-7908-1841-3_23 ◽

2000 ◽

pp. 265-273 ◽

Cited By ~ 1

Author(s):

Norihide Sano ◽

Ryoichi Takahashi

Keyword(s):

Natural Language ◽

Language Input ◽

Fuzzy Diagnosis

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Conversational agents in healthcare: a systematic review

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy072 ◽

2018 ◽

Vol 25 (9) ◽

pp. 1248-1258 ◽

Cited By ~ 146

Author(s):

Liliana Laranjo ◽

Adam G Dunn ◽

Huong Ly Tong ◽

Ahmet Baki Kocaballi ◽

Jessica Chen ◽

...

Keyword(s):

Systematic Review ◽

Natural Language ◽

Depression Symptoms ◽

Conversational Agent ◽

Conversational Agents ◽

Cross Sectional ◽

Evaluation Measures ◽

Health Related ◽

Quasi Experimental ◽

Language Input

Abstract Objective Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen’s kappa measured inter-coder agreement. Results The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.

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