scholarly journals Point at the Triple: Generation of Text Summaries from Knowledge Base Triples (Extended Abstract)

2020 ◽  
Author(s):  
Pavlos Vougiouklis ◽  
Eddy Maddalena ◽  
Jonathon Hare ◽  
Elena Simperl
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Data Driven ◽  
Open Domain ◽  
Fixed Target ◽  
Human Evaluation

We investigate the problem of generating natural language summaries from knowledge base triples. Our approach is based on a pointer-generator network, which, in addition to generating regular words from a fixed target vocabulary, is able to verbalise triples in several ways. We undertake an automatic and a human evaluation on single and open-domain summaries generation tasks. Both show that our approach significantly outperforms other data-driven baselines.

scholarly journals Point at the Triple: Generation of Text Summaries from Knowledge Base Triples

2020 ◽  
Vol 69 ◽  
pp. 1-31
Author(s):  
Pavlos Vougiouklis ◽  
Eddy Maddalena ◽  
Jonathon Hare ◽  
Elena Simperl
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Data Driven ◽  
Open Domain ◽  
Fixed Target ◽  
Human Evaluation

We investigate the problem of generating natural language summaries from knowledge base triples. Our approach is based on a pointer-generator network, which, in addition to generating regular words from a fixed target vocabulary, is able to verbalise triples in several ways. We undertake an automatic and a human evaluation on single and open-domain summaries generation tasks. Both show that our approach significantly outperforms other data-driven baselines.

scholarly journals A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations

2017 ◽  
Author(s):  
Kangqi Luo ◽  
Xusheng Luo ◽  
Xianyang Chen ◽  
Kenny Q. Zhu
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Knowledge Base ◽  
Search Algorithm ◽  
Knowledge Bases ◽  
Data Driven ◽  
Learning Approach ◽  
Data Driven Approach ◽  
Structured Knowledge

This paper studies the problem of discovering the structured knowledge representation of binary natural language relations.The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics.We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation. Evaluation results show that inferred schemas are able to represent precise semantics, and can be used to enrich manually crafted knowledge bases.

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.

scholarly journals Drawing dynamic geometry figures online with natural language for junior high school geometry

2012 ◽  
Vol 13 (5) ◽  
pp. 126 ◽  
Author(s):  
Wing-Kwong Wong ◽  
Sheng-Kai Yin ◽  
Chang-Zhe Yang
Keyword(s):  
Distance Learning ◽  
Natural Language ◽  
Knowledge Base ◽  
Junior High School ◽  
Dynamic Geometry ◽  
Preliminary Evaluation ◽  
Web Page ◽  
High School Geometry ◽  
Geometric Figures ◽  
Teachers And Students

<p>This paper presents a tool for drawing dynamic geometric figures by understanding the texts of geometry problems. With the tool, teachers and students can construct dynamic geometric figures on a web page by inputting a geometry problem in natural language. First we need to build the knowledge base for understanding geometry problems. With the help of the knowledge base engine InfoMap, geometric concepts are extracted from an input text. The concepts are then used to output a multistep JavaSketchpad script, which constructs the dynamic geometry figure on a web page. Finally, the system outputs the script as an HTML document that can be visualized and read with an internet browser. Furthermore, a preliminary evaluation of the tool showed that it produced correct dynamic geometric figures for over 90% of problems from textbooks. With such high accuracy, the system produced by this study can support distance learning for geometry students as well as distance learning in producing geometry content for instructors.<br /><br /></p>

scholarly journals Towards Open Domain Chatbots—A GRU Architecture for Data Driven Conversations

2018 ◽  
pp. 213-222 ◽  
Author(s):  
Åsmund Kamphaug ◽  
Ole-Christoffer Granmo ◽  
Morten Goodwin ◽  
Vladimir I. Zadorozhny
Keyword(s):  
Data Driven ◽  
Open Domain

Clinical Practice Ontology Automatic Learning from SOAP Reports

2016 ◽  
pp. 349-363
Author(s):  
David Mendes ◽  
Irene Pimenta Rodrigues ◽  
Carlos Fernandes Baeta
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Natural Language ◽  
Knowledge Base ◽  
Clinical Information ◽  
Knowledge Structure ◽  
Daily Routine ◽  
Automatic Learning ◽  
Controlled Natural Language ◽  
Practice Knowledge

We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.

Sign in / Sign up

Export Citation Format

Share Document