scholarly journals VNLP: Visible natural language processing

2021 ◽  
pp. 147387162110388
Author(s):  
Mohammad Alharbi ◽  
Matthew Roach ◽  
Tom Cheesman ◽  
Robert S Laramee
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Black Box ◽  
User Preferences ◽  
Text Similarity ◽  
Input Text ◽  
Visually Based ◽  
Pipeline Design

In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.

Computational Models of Language Within Context and Context-Sensitive Language Understanding

2006 ◽  
Vol 10 (6) ◽  
pp. 782-790 ◽  
Author(s):  
Noriko Ito ◽  
◽  
Toru Sugimoto ◽  
Yusuke Takahashi ◽  
Shino Iwashita ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Models ◽  
Contextual Analysis ◽  
Language Understanding ◽  
Context Sensitive ◽  
Input Text ◽  
Systemic Functional Linguistic ◽  
Machine Readable

We propose two computational models - one of a language within context based on systemic functional linguistic theory and one of context-sensitive language understanding. The model of a language within context called the Semiotic Base characterizes contextual, semantic, lexicogrammatical, and graphological aspects of input texts. The understanding process is divided into shallow and deep analyses. Shallow analysis consists of morphological and dependency analyses and word concept and case relation assignment, mainly by existing natural language processing tools and machine-readable dictionaries. Results are used to detect the contextual configuration of input text in contextual analysis. This is followed by deep analyses of lexicogrammar, semantics, and concepts, conducted by referencing a subset of resources related to the detected context. Our proposed models have been implemented in Java and verified by integrating them into such applications as dialog-based question-and-answer (Q&A).

scholarly journals Digiprescription: An Intelligent System to Enable Paperless Prescription using Mobile Computing and Natural-language Processing

2020 ◽  
Author(s):  
Richard Zhang ◽  
Mary Zhao ◽  
Yucheng Jiang ◽  
Sophadeth Rithya ◽  
Yu Sun
Keyword(s):  
Natural Language Processing ◽  
Side Effects ◽  
Natural Language ◽  
Mobile Computing ◽  
Language Processing ◽  
Intelligent System ◽  
Drug Information ◽  
The Elderly ◽  
Black Box ◽  
Patient’S Information

Through our app, it is aimed to teach and tell the patients how to use the drug properly taking off the chances of putting their lives in danger, especially the elderly. It is also efficient to give patients these instructions as well as saving lots of paper. Because of the law, every drug that is given from the pharmacy to the user includes a receipt that lists information of, patient’s information, drug information, insurance information, directions on taking the medicine (black box warning issued by FDA), medication details on how it works, side effects, storage rules, and etc. These pieces of information are crucial to patients, where it tells them how to use the drug properly, but most people would throw these receipts away, which is a risk as well as a waste. Through using this app, the patient can efficiently get information on how to properly use the drug. This application is also helpful, where the user can choose to set reminders on when to eat this drug each week or month.

Deliberative Democracy in an Unequal World: AText-As-DataStudy of South India’s Village Assemblies

2019 ◽  
Vol 113 (3) ◽  
pp. 623-640 ◽  
Author(s):  
RAMYA PARTHASARATHY ◽  
VIJAYENDRA RAO ◽  
NETHRA PALANISWAMY
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Deliberative Democracy ◽  
Language Processing ◽  
Real World ◽  
Local Development ◽  
Black Box ◽  
Rural India ◽  
Processing Methods ◽  
Indian Context

This paper opens the “black box” of real-world deliberation by usingtext-as-datamethods on a corpus of transcripts from the constitutionally mandatedgram sabhas, or village assemblies, of rural India. Drawing on normative theories of deliberation, we identify empirical standards for “good” deliberation based on one’s ability both to speak and to be heard, and use natural language processing methods to generate these measures. We first show that, even in the rural Indian context, these assemblies are not mere “talking shops,” but rather provide opportunities for citizens to challenge their elected officials, demand transparency, and provide information about local development needs. Second, we find that women are at a disadvantage relative to men; they are less likely to speak, set the agenda, and receive a relevant response from state officials. And finally, we show that quotas for women for village presidencies improve the likelihood that female citizens are heard.

scholarly journals Voice Controlled Home Automation Using AI and NLP

2021 ◽  
pp. 195-197
Author(s):  
P. Navaraja ◽  
P. Kishore ◽  
S. Dineshkumar ◽  
R. Karthick ◽  
C. Kavinkumar
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Disabled Persons ◽  
Vital Role ◽  
Primary Objective ◽  
User Preferences ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Home Automation

The aim of home automation is to make our lives easier and to improve the quality of life. The concept of Smart Homes builds on the progressing maturity of areas such as Artificial Intelligence and Natural Language Processing. Here, natural language processing (NLP) plays a vital role since it acts as an interface between human interaction and machines. Through NLP users can either command or control devices at home even though disabled persons command or request varies from presets. An application area of AI is Natural Language Processing (NLP). Voice assistants incorporate AI using cloud computing and can communicate with the users in natural language. Voice assistants are easy to use and thus there are millions of devices that incorporate them in households nowadays. Our project aims at providing a fully automated voice based solution that our users can rely on, to perform more than just switching on/off the appliances. The user sends a command through speech to the mobile device, which interprets the message and sends the appropriate command to the specific appliance. The primary objective is to construct a useful voice-based system that utilizes AI and NLP to control all domestic applications and services and also learn the user preferences over time using machine learning algorithms.

scholarly journals An Overview of the Basic NLP Resources Towards Building the Assamese-English Machine Translation

2021 ◽  
Author(s):  
Nibedita Roy ◽  
Apurbalal Senapati
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Daily Life ◽  
Point Of View ◽  
Advanced Stage ◽  
Computational Point ◽  
Input Text ◽  
Level Performance

Machine Translation (MT) is the process of automatically converting one natural language into another, preserving the exact meaning of the input text to the output text. It is one of the classical problems in the Natural Language Processing (NLP) domain and there is a wide application in our daily life. Though the research in MT in English and some other language is relatively in an advanced stage, but for most of the languages, it is far from the human-level performance in the translation task. From the computational point of view, for MT a lot of preprocessing and basic NLP tools and resources are needed. This study gives an overview of the available basic NLP resources in the context of Assamese-English machine translation.

An Improved Text Similarity Calculation Algorithm Based on VSM

2011 ◽  
Vol 225-226 ◽  
pp. 1105-1108
Author(s):  
Lian Li ◽  
Ai Hong Zhu ◽  
Tao Su
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Clustering ◽  
Experimental Results ◽  
Measured Parameter ◽  
Text Similarity ◽  
Calculation Algorithm ◽  
Key Technology ◽  
Similarity Calculation

Text similarity calculation is a key technology in the fields of text clustering, Web intelligent retrieval and natural language processing etc. Because the traditional text similarity calculation algorithm does not consider the affect of same feature words between texts, sometimes this algorithm may lead to inaccurate results. To solve this problem, this paper gives an improved text similarity calculation algorithm. Considering that the amount of same feature words reflects two texts’ similarity in some extent, the improved algorithm adds in the coverage measured parameter, which effectively reduces the interference of texts with lower similarity. The simulation and experimental results verify the improved algorithm’s correctness and effectiveness.

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