Geographical localization of web domains and organization addresses recognition by employing natural language processing, Pattern Matching and clustering

2016 ◽  
Vol 51 ◽  
pp. 202-211 ◽  
Author(s):  
Paolo Nesi ◽  
Gianni Pantaleo ◽  
Marco Tenti
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Pattern Matching ◽  
Language Processing

Developing Chatbots for Supporting Health Self-Management

2021 ◽  
pp. 135-156
Author(s):  
Jesús Fernández-Avelino ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Laura Nely Sánchez-Morales
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Pattern Matching ◽  
Language Processing ◽  
Self Management ◽  
Customer Base ◽  
Design And Implementation ◽  
Main Components ◽  
Answering Questions

A chatbot is a software agent that mimics human conversation using artificial intelligence technologies. Chatbots help to accomplish tasks ranging from answering questions, playing music, to managing smart home devices. The adoption of this kind of agent is increasing since people are discovering the benefits of them, such as saving time and money, higher customer satisfaction, customer base growing, among others. However, developing a chatbot is a challenging task that requires addressing several issues such as pattern matching, natural language understanding, and natural language processing, as well as to design a knowledge base that encapsulates the intelligence of the system. This chapter describes the design and implementation of a text/speech chatbot for supporting health self-management. This chatbot is currently based on Spanish. The main goal of this chapter is to clearly describe the main components and phases of the chatbot development process, the methods, and tools used for this purpose, as well as to describe and discuss our findings from the practice side of things.

PENERAPAN NATURAL LANGUAGE PROCESSING BERBASIS VIRTUAL ASSISTANT PADA BAGIAN ADMINISTRASI AKADEMIK STMIK DHARMA WACANA

2021 ◽  
Vol 5 (1) ◽  
pp. 33-47
Author(s):  
Arief Adjie Wicaksono ◽  
Ridwan Yusuf ◽  
Tri Aristi Saputri
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Pattern Matching ◽  
Language Processing ◽  
Regular Expression

Sekolah Tinggi Ilmu Manajemen Informatika dan Komputer (STMIK) Dharma Wacana memiliki beberapa bagian seperti Bagian Administrasi Akademik yang memiliki tugas melaksanakan pelayanan dibidang akademik. Bagian Administrasi Akademik menjadi sumber informasi terkait kegiatan perkuliahan. Kebutuhan informasi perkuliahan belum efektif dikarenakan terbatasnya jam kerja dari pegawai dan masih banyak pertanyaan berulang yang berdatangan ke Bagian Administrasi Akademik, seperti pertanyaan yang telah ditanyakan oleh seorang mahasiswa kemudian ditanyakan lagi oleh mahasiswa lainnya. Tujuan dari penelitian ini adalah melakukan observasi dan wawancara terhadap mahasiswa dan pegawai Bagian Administrasi Akademik serta menganalisis kelemahannya sehingga dapat menjadi acuan untuk merancang aplikasi dengan penerapan Natural Language Processing (NLP). Pada penelitian telah dibangun Virtual Assistant berupa Chatbot yang tersedia pada platform messenger yaitu LINE, Facebook dan Telegram yang hanya bertindak layaknya bagian informasi perkuliahan. NLP dengan pendekatan pattern matching menggunakan regular expression diterapkan dalam proses mengenali pertanyaan mahasiswa sehingga Virtual Assistant dapat memberikan jawaban yang sesuai.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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