scholarly journals Composing Questions through Conceptual Authoring

2007 ◽  
Vol 33 (1) ◽  
pp. 105-133 ◽  
Author(s):  
Catalina Hallett ◽  
Donia Scott ◽  
Richard Power
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Free Text ◽  
Risk Averse ◽  
Proof Of Concept ◽  
Concept System ◽  
Complex Queries ◽  
Extensive Training ◽  
Question Answering Systems ◽  
Medical Histories

This article describes a method for composing fluent and complex natural language questions, while avoiding the standard pitfalls of free text queries. The method, based on Conceptual Authoring, is targeted at question-answering systems where reliability and transparency are critical, and where users cannot be expected to undergo extensive training in question composition. This scenario is found in most corporate domains, especially in applications that are risk-averse. We present a proof-of-concept system we have developed: a question-answering interface to a large repository of medical histories in the area of cancer. We show that the method allows users to successfully and reliably compose complex queries with minimal training.

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

A Review on Question Generation from Natural Language Text

2022 ◽  
Vol 40 (1) ◽  
pp. 1-43
Author(s):  
Ruqing Zhang ◽  
Jiafeng Guo ◽  
Lu Chen ◽  
Yixing Fan ◽  
Xueqi Cheng
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Data Augmentation ◽  
Text Structure ◽  
Current Status ◽  
Question Generation ◽  
Natural Language Text ◽  
Question Answering Systems ◽  
The Right ◽  
Language Text

Question generation is an important yet challenging problem in Artificial Intelligence (AI), which aims to generate natural and relevant questions from various input formats, e.g., natural language text, structure database, knowledge base, and image. In this article, we focus on question generation from natural language text, which has received tremendous interest in recent years due to the widespread applications such as data augmentation for question answering systems. During the past decades, many different question generation models have been proposed, from traditional rule-based methods to advanced neural network-based methods. Since there have been a large variety of research works proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we try to provide a more comprehensive taxonomy of question generation tasks from three different perspectives, i.e., the types of the input context text, the target answer, and the generated question. We take a deep look into existing models from different dimensions to analyze their underlying ideas, major design principles, and training strategies We compare these models through benchmark tasks to obtain an empirical understanding of the existing techniques. Moreover, we discuss what is missing in the current literature and what are the promising and desired future directions.

A Proof of Concept for Assessing Emergency Room Use with Primary Care Data and Natural Language Processing

2013 ◽  
Vol 52 (01) ◽  
pp. 33-42 ◽  
Author(s):  
M.-H. Kuo ◽  
P. Gooch ◽  
J. St-Maurice
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Emergency Room ◽  
Language Processing ◽  
Free Text ◽  
Proof Of Concept ◽  
Term Extraction ◽  
Primary Care Data

SummaryObjective: The objective of this study was to undertake a proof of concept that demonstrated the use of primary care data and natural language processing and term extraction to assess emergency room use. The study extracted biopsychosocial concepts from primary care free text and related them to inappropriate emergency room use through the use of odds ratios.Methods: De-identified free text notes were extracted from a primary care clinic in Guelph, Ontario and analyzed with a software toolkit that incorporated General Architecture for Text Engineering (GATE) and MetaMap components for natural language processing and term extraction.Results: Over 10 million concepts were extracted from 13,836 patient records. Codes found in at least 1% percent of the sample were regressed against inappropriate emergency room use. 77 codes fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related concepts.Conclusions: Analyzed thematically, mental health issues and pain are important themes; we have concluded that pain and mental health problems are primary drivers for inappropriate emergency room use. Age and sex were not significant. This proof of concept demonstrates the feasibly of combining natural language processing and primary care data to analyze a system use question. As a first work it supports further research and could be applied to investigate other, more complex problems.

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.  

scholarly journals MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs

Semantic Web ◽  
2021 ◽  
pp. 1-17
Author(s):  
Lucia Siciliani ◽  
Pierpaolo Basile ◽  
Pasquale Lops ◽  
Giovanni Semeraro
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Query Language ◽  
State Of The Art ◽  
Translation Process ◽  
Specific Data ◽  
Data Query ◽  
Question Answering Systems ◽  
Knowledge Graphs ◽  
The Impact

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.

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

scholarly journals Neural factoid geospatial question answering

2021 ◽  
pp. 65-90
Author(s):  
Haonan Li ◽  
Ehsan Hamzei ◽  
Ivan Majic ◽  
Hua Hua ◽  
Jochen Renz ◽  
...  
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Query Language ◽  
Graph Representation ◽  
Geospatial Information ◽  
Graph Representations ◽  
Semantic Graph ◽  
Complex Syntax ◽  
Question Answering Systems ◽  
Semantic Encoding

Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.

Using a Dialogue Manager to Improve Semantic Web Search

2016 ◽  
Vol 12 (1) ◽  
pp. 62-78 ◽  
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
System Performance ◽  
Web Search ◽  
Question Answering ◽  
Similar Question ◽  
User Intentions ◽  
Question Answering Systems ◽  
Dialogue Manager

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors' system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

scholarly journals Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base

2020 ◽  
Vol 12 (3) ◽  
pp. 45
Author(s):  
Wenqing Wu ◽  
Zhenfang Zhu ◽  
Qiang Lu ◽  
Dianyuan Zhang ◽  
Qiangqiang Guo
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Temporal Information ◽  
Temporal Constraints ◽  
External Knowledge ◽  
Question Answering Systems ◽  
Natural Language Question ◽  
Applied Knowledge

Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.

Sign in / Sign up

Export Citation Format

Share Document