scholarly journals Constructing a corpus-informed list of Arabic formulaic sequences (ArFSs) for language pedagogy and technology

2019 ◽  
Vol 24 (2) ◽  
pp. 202-228
Author(s):  
Ayman Alghamdi ◽  
Eric Atwell
Keyword(s):  
Natural Language Processing ◽  
Mixed Methods ◽  
Natural Language ◽  
Language Processing ◽  
Native Speakers ◽  
Formulaic Sequences ◽  
Learning And Teaching ◽  
Language Pedagogy ◽  
Language Resource

Abstract This study aims to construct a corpus-informed list of Arabic Formulaic Sequences (ArFSs) for use in language pedagogy (LP) and Natural Language Processing (NLP) applications. A hybrid mixed methods model was adopted for extracting ArFSs from a corpus, that combined automatic and manual extracting methods, based on well-established quantitative and qualitative criteria that are relevant from the perspective of LP and NLP. The pedagogical implications of this list are examined to facilitate the inclusion of ArFSs in the process of learning and teaching Arabic, particularly for non-native speakers. The computational implications of the ArFSs list are related to the key role of the ArFSs as a novel language resource in the improvement of various Arabic NLP tasks.

scholarly journals Reasoning about Quantities in Natural Language

2015 ◽  
Vol 3 ◽  
pp. 1-13 ◽  
Author(s):  
Subhro Roy ◽  
Tim Vieira ◽  
Dan Roth
Keyword(s):  
Elementary School ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Approach ◽  
Quantitative Reasoning ◽  
Language Understanding ◽  
Numerical Reasoning ◽  
Key Steps

Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.

scholarly journals An Emerging Role of Chatbot in Businesses as a Novel Interactive Tool

2019 ◽  
Vol 7 (2) ◽  
pp. 36-39
Author(s):  
T. Venkat Narayana Rao et al.
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Customer Service ◽  
Language Processing ◽  
Machine Language ◽  
Business Sector ◽  
Business People ◽  
History Of

Chatbot enables the business people to reach their target customers using popular messenger apps like Facebook, Whatsapp etc. Chatbots are not handled by humans directly. Nowadays, Chatbots are becoming very popular especially in business sector by reducing the human efforts and automated customer service. It is a software which interacts with user using natural language processing, Machine Language and Artificial Intelligence. They allow users to simply ask questions which would simulate interaction with the humans. The popular and well known chatbots are Alex and Siri. This paper focus on review of chatbot, history of chatbot and its implementation along with applications.

Alternative Ways for Knowledge Collection, Indexing and Robust Language Retrieval

1998 ◽  
Vol 37 (04/05) ◽  
pp. 315-326 ◽  
Author(s):  
C. Lovis ◽  
A.-M. Rassinoux ◽  
J.-R. Scherrer ◽  
R. H. Baud
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Building Blocks ◽  
Linguistic Knowledge ◽  
Recent Experience ◽  
Medical Texts ◽  
Natural Components

AbstractDefinitions are provided of the key entities in knowledge representation for Natural Language Processing (NLP). Starting from the words, which are the natural components of any sentence, both the role of expressions and the decomposition of words into their parts are emphasized. This leads to the notion of concepts, which are either primitive or composite depending on the model where they are created. The problem of finding the most adequate degree of granularity for a concept is studied. From this reflection on basic Natural Language Processing components, four categories of linguistic knowledge are recognized, that are considered to be the building blocks of a Medical Linguistic Knowledge Base (MLKB). Following on the tracks of a recent experience in building a natural language-based patient encoding browser, a robust method for conceptual indexing and query of medical texts is presented with particular attention to the scheme of knowledge representation.

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