Natural Language Understanding: Language is more than words; "meaning" depends on context, and "understanding" requires a vast body of knowledge about the world

Abstract As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for renewed attention to the dynamics between research in automatic speech recognition (ASR) and natural language understanding (NLU). We briefly review these research areas and lay out the current relationship between them. In light of the observations we make in this paper, we argue that (1) NLU should be cognizant of the presence of ASR models being used upstream in a dialog system’s pipeline, (2) ASR should be able to learn from errors found in NLU, (3) there is a need for end-to-end datasets that provide semantic annotations on spoken input, (4) there should be stronger collaboration between ASR and NLU research communities.

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From NLP to Natural Language Understanding for medical decision making

PsycEXTRA Dataset ◽

10.1037/e615572012-006 ◽

2012 ◽

Author(s):

Chitta Baral

Keyword(s):

Decision Making ◽

Natural Language ◽

Medical Decision Making ◽

Natural Language Understanding ◽

Medical Decision ◽

Language Understanding

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The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

Methods of Information in Medicine ◽

10.1055/s-0038-1634568 ◽

1998 ◽

Vol 37 (04/05) ◽

pp. 327-333 ◽

Cited By ~ 3

Author(s):

F. Buekens ◽

G. De Moor ◽

A. Waagmeester ◽

W. Ceusters

Keyword(s):

Natural Language ◽

Knowledge Acquisition ◽

Natural Language Understanding ◽

Knowledge Bases ◽

Linguistic Knowledge ◽

Medical Terminology ◽

Language Understanding ◽

Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

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Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

Methods of Information in Medicine ◽

10.1055/s-0038-1634611 ◽

1995 ◽

Vol 34 (04) ◽

pp. 345-351 ◽

Cited By ~ 11

Author(s):

A. Burgun ◽

L. P. Seka ◽

D. Delamarre ◽

P. Le Beux

Keyword(s):

Natural Language ◽

Patient Discharge ◽

Natural Language Understanding ◽

Assessment Procedure ◽

Coding System ◽

Language Understanding ◽

Processing Scheme ◽

Automated Coding ◽

Conceptual Graph ◽

Discharge Summaries

Abstract:In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

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