scholarly journals CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots

2019 ◽  
Author(s):  
Arshit Gupta ◽  
Peng Zhang ◽  
Garima Lalwani ◽  
Mona Diab
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Context Aware ◽  
Language Understanding ◽  
Task Oriented

scholarly journals TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue

2020 ◽  
Author(s):  
Chien-Sheng Wu ◽  
Steven C.H. Hoi ◽  
Richard Socher ◽  
Caiming Xiong
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Task Oriented

scholarly journals Natural language understanding of map navigation queries in Roman Urdu by joint entity and intent determination

2021 ◽  
Vol 7 ◽  
pp. e615
Author(s):  
Javeria Hassan ◽  
Muhammad Ali Tahir ◽  
Adnan Ali
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Dialogue Systems ◽  
Word Embeddings ◽  
Dialogue System ◽  
Language Understanding ◽  
Input Sentence ◽  
Navigation Software ◽  
Task Oriented ◽  
Natural Way

Navigation based task-oriented dialogue systems provide users with a natural way of communicating with maps and navigation software. Natural language understanding (NLU) is the first step for a task-oriented dialogue system. It extracts the important entities (slot tagging) from the user’s utterance and determines the user’s objective (intent determination). Word embeddings are the distributed representations of the input sentence, and encompass the sentence’s semantic and syntactic representations. We created the word embeddings using different methods like FastText, ELMO, BERT and XLNET; and studied their effect on the natural language understanding output. Experiments are performed on the Roman Urdu navigation utterances dataset. The results show that for the intent determination task XLNET based word embeddings outperform other methods; while for the task of slot tagging FastText and XLNET based word embeddings have much better accuracy in comparison to other approaches.

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

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
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.

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

1995 ◽  
Vol 34 (04) ◽  
pp. 345-351 ◽  
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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