scholarly journals Super Agent Chatbot “3S” Sebagai Media Informasi Menggunakan Metoda Natural Language Processing(NLP)

2019 ◽  
Vol 2 (1) ◽  
pp. 53-64
Author(s):  
Herwin H Herwin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Site ◽  
Question Answering ◽  
Question Answering Systems ◽  
Portal Website

STMIK Amik Riau memiliki portal pada website http://www.sar.ac.id difungsikan sebagai media penyebaran informasi bagi sivitas akademika dan stakeholder. Rerata pengunjung setiap hari dalam 3 bulan terakhir adalah 150 kunjungan, namun terjadi peningkatan pada saat penerimaan mahasiswa di setiap tahun akademik. Hal ini mengindikasikan terjadinya peningkatan minat masyarakat untuk mengetahui informasi STMIK Amik Riau. Sayangnya, sampai saat ini pemanfaatan portal web site masih satu arah, dari STMIK Amik Riau ke stakeholder dan masyarakat, tidak terjadi sebaliknya. Komunikasi stakeholder dengan PT sehubungan dengan muatan yang ada di dalam portal menggunakan media sosial dan tidak terintegrasi dengan web.  Begitu juga dengan masukan, koreksi, tanggapan, maupun komunikasi lain menggunakan media sosial.  Sampai saat ini, masyarakat yang mengunjungi portal website baik masyarakat luas, maupun stakeholder tidak dapat dideteksi waktu berkunjung sehingga tidak dapat disapa dengan filosofi “3S”, padahal masyarakat luas yang telah berkunjung merupakan pasar potensial untuk di edukasi. Masyarakat yang berkunjung ke portal website, dengan sopan di sapa oleh sistem, kemudian dilanjutkan dengan komunikasi langsung, tersedia mesin yang siap memberikan salam  dan melayani setiap pertanyaan yang diajukan oleh pengunjung. Penelitian ini bertujuan membuat chatbot yang mampu berkomunikasi dengan pengunjung website.  Chatbot  yang telah dibuat diberi nama STMIK Amik Riau Intelligence Virtual Information disingkat SILVI.  Chatbot dibuat berdasarkan Question Answering Systems (QAS), bekerja dengan algoritma kemiripan antara dua teks. Penelitian ini menghasilkan aplikasi yang siap digunakan, diberi nama SILVI, mampu berkomunikasi dengan pengunjung website. Chatbot mengoptimalkan komunikasi seolah tidak menyadari, tetap menganggap lawan bicara adalah pegawai yang tepat dalam tugas pokok dan fungsi.  

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Building Graph for Events and Time in Natural Language Text

2020 ◽  
Vol 9 (3) ◽  
pp. 581-586
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Relation Extraction ◽  
Event Extraction ◽  
Event Time ◽  
Time Graph ◽  
Question Answering Systems

Events and time are two major key terms in natural language processing due to the various event-oriented tasks these are become an essential terms in information extraction. In natural language processing and information extraction or retrieval event and time leads to several applications like text summaries, documents summaries, and question answering systems. In this paper, we present events-time graph as a new way of construction for event-time based information from text. In this event-time graph nodes are events, whereas edges represent the temporal and co-reference relations between events. In many of the previous researches of natural language processing mainly individually focused on extraction tasks and in domain-specific way but in this work we present extraction and representation of the relationship between events- time by representing with event time graph construction. Our overall system construction is in three-step process that performs event extraction, time extraction, and representing relation extraction. Each step is at a performance level comparable with the state of the art. We present Event extraction on MUC data corpus annotated with events mentions on which we train and evaluate our model. Next, we present time extraction the model of times tested for several news articles from Wikipedia corpus. Next is to represent event time relation by representation by next constructing event time graphs. Finally, we evaluate the overall quality of event graphs with the evaluation metrics and conclude the observations of the entire work

A Brief Survey of Question Answering Systems

2021 ◽  
Vol 12 (5) ◽  
pp. 01-07
Author(s):  
Michael Caballero
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Open Domain ◽  
Knowledge Based ◽  
Current State ◽  
Introductory Overview ◽  
Building Systems ◽  
Question Answering Systems

Question Answering (QA) is a subfield of Natural Language Processing (NLP) and computer science focused on building systems that automatically answer questions from humans in natural language. This survey summarizes the history and current state of the field and is intended as an introductory overview of QA systems. After discussing QA history, this paper summarizes the different approaches to the architecture of QA systems -- whether they are closed or open-domain and whether they are text-based, knowledge-based, or hybrid systems. Lastly, some common datasets in this field are introduced and different evaluation metrics are discussed.

A survey of question answering in natural language processing

Poetics ◽  
1990 ◽  
Vol 19 (1-2) ◽  
pp. 99-120
Author(s):  
Stefan Wermter ◽  
Wendy G. Lehnert
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering

scholarly journals Enhancing Natural Language Inference Using New and Expanded Training Data Sets and New Learning Models

2020 ◽  
Vol 34 (05) ◽  
pp. 8504-8511
Author(s):  
Arindam Mitra ◽  
Ishan Shrivastava ◽  
Chitta Baral
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Training Data ◽  
Data Sets ◽  
Learning Models ◽  
New Learning ◽  
Word Attention ◽  
Attention Function

Natural Language Inference (NLI) plays an important role in many natural language processing tasks such as question answering. However, existing NLI modules that are trained on existing NLI datasets have several drawbacks. For example, they do not capture the notion of entity and role well and often end up making mistakes such as “Peter signed a deal” can be inferred from “John signed a deal”. As part of this work, we have developed two datasets that help mitigate such issues and make the systems better at understanding the notion of “entities” and “roles”. After training the existing models on the new dataset we observe that the existing models do not perform well on one of the new benchmark. We then propose a modification to the “word-to-word” attention function which has been uniformly reused across several popular NLI architectures. The resulting models perform as well as their unmodified counterparts on the existing benchmarks and perform significantly well on the new benchmarks that emphasize “roles” and “entities”.

LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

2021 ◽  
pp. 2150006
Author(s):  
Saravanakumar Kandasamy ◽  
Aswani Kumar Cherukuri
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Gold Standard ◽  
Question Answering ◽  
Knowledge Based ◽  
Benchmark Datasets ◽  
Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

scholarly journals An Empirical Study of Content Understanding in Conversational Question Answering

2020 ◽  
Vol 34 (05) ◽  
pp. 7578-7585
Author(s):  
Ting-Rui Chiang ◽  
Hao-Tong Ye ◽  
Yun-Nung Chen
Keyword(s):  
Natural Language Processing ◽  
Empirical Study ◽  
Language Processing ◽  
Question Answering ◽  
Source Code ◽  
Content Understanding ◽  
Question Answering Systems ◽  
Benchmark Datasets ◽  
Context Free ◽  
Answering Questions

With a lot of work about context-free question answering systems, there is an emerging trend of conversational question answering models in the natural language processing field. Thanks to the recently collected datasets, including QuAC and CoQA, there has been more work on conversational question answering, and recent work has achieved competitive performance on both datasets. However, to best of our knowledge, two important questions for conversational comprehension research have not been well studied: 1) How well can the benchmark dataset reflect models' content understanding? 2) Do the models well utilize the conversation content when answering questions? To investigate these questions, we design different training settings, testing settings, as well as an attack to verify the models' capability of content understanding on QuAC and CoQA. The experimental results indicate some potential hazards in the benchmark datasets, QuAC and CoQA, for conversational comprehension research. Our analysis also sheds light on both what models may learn and how datasets may bias the models. With deep investigation of the task, it is believed that this work can benefit the future progress of conversation comprehension. The source code is available at https://github.com/MiuLab/CQA-Study.

scholarly journals Basic challenges in natural language processing systems

2020 ◽  
pp. 41-45
Author(s):  
O. Hyryn
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Search ◽  
Question Answering ◽  
Syntactic Analysis ◽  
Anaphora Resolution ◽  
Grammatical Structure ◽  
English Sentence ◽  
Improved Model

The article proceeds from the intended use of parsing for the purposes of automatic information search, question answering, logical conclusions, authorship verification, text authenticity verification, grammar check, natural language synthesis and other related tasks, such as ungrammatical speech analysis, morphological class definition, anaphora resolution etc. The study covers natural language processing challenges, namely of an English sentence. The article describes formal and linguistic problems, which might arise during the process and which are connected with graphic, semantic, and syntactic ambiguity. The article provides the description of how the problems had been solved before the automatic syntactic analysis was applied and the way, such analysis methods could be helpful in developing new analysis algorithms today. The analysis focuses on the issues, blocking the basis for the natural language processing — parsing — the process of sentence analysis according to their structure, content and meaning, which aims to examine the grammatical structure of the sentence, the division of sentences into constituent components and defining links between them. The analysis identifies a number of linguistic issues that will contribute to the development of an improved model of automatic syntactic analysis: lexical and grammatical synonymy and homonymy, hypo- and hyperonymy, lexical and semantic fields, anaphora resolution, ellipsis, inversion etc. The scope of natural language processing reveals obvious directions for the improvement of parsing models. The improvement will consequently expand the scope and improve the results in areas that already employ automatic parsing. Indispensable achievements in vocabulary and morphology processing shall not be neglected while improving automatic syntactic analysis mechanisms for natural languages.

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