A practical goal for natural language text generation research is to converge on a separation of functions into modules that can be independently re-used . This paper addresses issues related to software re-use and evolution in text generation systems . We describe the benefits we obtained by adapting and generalizing the generation modules and techniques we used for the successive development of three distinct text generation applications , PLANDoc , FLOWDoc , and ZEDDoc . We are currently implementing CIRCSIM-Tutor v. 3 , a natural-language based intelligent tutoring system ( ITS ) which tutors medical students on a topic in cardiovascular physiology ( the baroreceptor reflex ) . We outline the text generation process in CIRCSIM-Tutor and illustrate a number of issues we have faced with respect to the representation of communicative action . For that reason , spelling correction is needed to solve any writing mistakes . This design process discuss about the making of spelling correction for document text in Indonesian language with document 's text as its input and a .txt file as its output . For the realization , 5 000 news articles have been used as training data . Spelling error correction is a Natural Language Processing ( NLP ) problem , and it has recently become relevant because many of the potential NLP applications such as text summarization , sentiment analysis and machine translation etc take advantage of spelling error analysis . Spell checking is a well-known task in NLP . During UVL analysis we deal with domain-speci c terms in the input and we also support spelling error correction of those terms . Besides this , we apply the following three adaptive techniques : ( 1 ) the use of the Japanese-English dictionary EDICT as secondary lexicon for the retrieval of information about unknown words and the automatic generation of new lexical entries , ( 2 ) the semi-automatic extraction of domain-speci c terms from domain-speci c documents , and ( 3 ) the application of pronunciation data from the Japanese character le KANJIDIC to improve the precision of spelling error correction . The semantic component generates the semantic representation of the input sentence . Similarly , OCR output contains almost exclusively substitution errors , which ordinarily account for less than 20 percent of keyboarded misspellings [ 9 , 10 ] . This paper describes the correction pro.gram developed as part of SPEEDCOP ( Spelling Error . Detection/ © 1984ACM0001 - 0782/84/0400 - 0358 75¢ Correction Project ) , a Chemical Abstracts Service ( CAS ) project supported by the National Science Foundation . One kind of such preprocessing is automatic word spacing . This process decides correct boundaries between words in a sentence containing spacing errors , which are a type of spelling error . Except for some Asian languages such as Chinese and Japanese , most languages have explicit word spacing . We read James L. Peterson 's article , “ Computer Programs for Detecting and Correcting Spelling Errors ” with great interest . We too have recently developed a spelling correction program as part of a tutorial on programming techniques and style . Our basic modesty inhibited us from publishing the details — there was , after all , little research involved in making the program . Our spelling correction program is part of a system for understanding illformed input in an intelligent tutoring system for medical students . Speed and user-friendliness were the most important considerations in the design . In Chinese , there are many characters which are similar in shape , and this phenomenon usually induces writing errors . As one important issue in spelling automatic correction , shape similarity measurement is still a challenging problem . To address this issue , we propose a component-tree based method in this paper , which is based on the hypothesis " characters are similar if their construction and components are both similar " . It proposes interaction elements to support functionalities that assists user during query formulation . These functionalities include domain filter suggestion , query suggestion , and spelling correction functions . Tamil language has an agglutinative , diglossic , alpha-syllabary structure which provides a significant combinatorial explosion of morphological forms all of which are effectively used in Tamil prose , poetry from antiquity to the modern age in an unbroken chain of continuity . However , for the language understanding , spelling correction purposes some of these present challenges as out-of-dictionary words . In this paper the authors propose algorithmic techniques to handle specific problems of conjoined-words ( out-of-dictionary ) (transliteration)[thendRalkattRu ] = [thendRal]+[kattRu ] when parts are alone present in word-list in efficient way . We refer to this method as REINFORCE - critic . subsection : Spelling Correction We use text from the One Billion Word dataset for the spelling correction task chelba2013one , which has pre - defined training and testing sets . The training data was abundant , and we never used any example twice . Online news and social media are transforming the process of news production and reading . While research has shown that news media play an important role in providing information to the public in democratic societies , research investigating the impact of sharing news online on the process of public opinion formation is in a nascent stage . Social media monitoring is now an essential part of brand management , political science , and news production . Automatic tweet ranking and content recommendation methods are required , in order to support human analysts in deriving useful insights from large-scale social media data . It implies many possible combinations of formerly discrete realms — print , television , radio and online media — and has an impact on the organisation of newsrooms with joint newsrooms for all channels and journalists working for more than one platform . Furthermore convergence changes the storytelling in journalism , because the Internet is a central hub of text , imagery , video and audio and is not only a content platform but also a distribution platform for conventional media — with active users taking part at the news process . Also the far-reaching consequences for the job profile and the education of journalists are discussed in this article . Journalists retrieve information from a complex range of sources . Addresses the information sources which are used in the news process . Examines the preference for human sources as opposed to library‐based information and discusses the influence of pragmatic constraints like time and space on the production of news . The thinking creation in news edition is the presupposition of the creation of news report . The thinking creation in news creation alone can be the creation of forms and contents in news report . As the exponential growth of news creation on the internet , the amount of digital news has reached out billion numbers . Digital news is naturally linked each other but it needs to be grouped so that user can easily classify the news that they read . Automatic Document Summarisation plays a central role in the process of providing the user with a quick access to information . Applications range from the generation of news headlines , to the aggregation of opinions extracted from reviews . Traditional topic-based summarisation systems are not always able to capture the sentiments expressed in a review . Personalized Recommendation System Based on PRML for E-Commerce.- An Efficient Index Scheme for XML Databases.- On Separating Constant from Polynomial Ambiguity of Finite Automata.- Reliable Broadcasting Without Collision Detection.- Semi-strong Static Type Checking of Object-Oriented Query Languages.- Building a Fuzzy Transformation System.- News Generating Via Fuzzy Summarization of Databases.- Improving Web Sites with Web Usage Mining , Web Content Mining , and Semantic Analysis.- Automatic Distribution of Sequential Code Using JavaSymphony Middleware.- Unifying Framework for Message Passing.- Heuristics on the Definition of UML Refinement Patterns.- The Complexity of Problems on Implicitly Represented Inputs.- An Improved News Generating . The paper introduces an improved method of intelligent summarization of large datasets . We performed a second analysis by comparing the quality of text expansion in English to Spanish , using an ad-hoc Spanish-English parallel corpus . The system might also be applied to other domains such as report and news generation . Therefore , we design agent-based model of opinion dynamics in social media that links information consumption and the formation of echo chamber . The model focuses on users interaction with news and the subsequent dynamics of echo chamber formation by presenting different cases of news generation and users behavior in SNS . The result is presented as suggestions to reduce the presence of echo chamber in SNS . This workshop wanted to stress the vision of this powerful communication channel as a social sensor , which can be used to detect and characterize interesting and yet unreported information and events in real time , crossing all topics and locations . Future technologies on this connectivity may also provide applications with automatic techniques for the generation of news ( filtered over user profiles ) , offering a sideways to the existing authoritative information media . It is the human that is the initial target , as well as the first line of defense . At the same time , social media has become a dominant , direct , and highly effective form of news generation and sharing at a global scale , in a manner that influences and enhances , but also challenges and often antagonizes , traditional media corporations . Document summarization is of great value to many real world applications , such as snippets generation for search results and news headlines generation . Traditionally , document summarization is implemented by extracting sentences that cover the main topics of a document with a minimum redundancy . The case study is the Brazilian politics scenario starting at the 2018 general elections on Twitter and WhatsApp . These two platforms were a fertile ground to fake news proliferation . We also evaluated the models over another available dataset from literature . In recent years , with the development of deep learning technology , text generation technology has undergone great changes and provided many kinds of services for human beings , such as restaurant reservation and daily communication . The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text generation technology , that is the conditional text generation , including emotional text generation , personalized text generation , and so on . Conditional text generation ( c-TextGen ) has thus become a research hotspot . In this paper , we focus on addressing the limitations caused by having to specify blanks to be filled . We decompose conditional text generation problem into two tasks , make-a-blank and fill-in-the-blank , and extend the former to handle more complex manipulations on the given tokens . We cast these tasks as a hierarchical multi agent RL problem and introduce a conditional adversarial learning that allows the agents to reach a goal , producing realistic texts , in cooperative setting . Generating and Modifying Images Based on Continual Linguistic Instruction . Conditional text-to-image generation is an active area of research , with many possible applications . Existing research has primarily focused on generating a single image from available conditioning information in one step . In particular , a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during model inference . Experimental results show that our TGVAE outperforms alternative approaches on both unconditional and conditional text generation , which can generate semantically-meaningful sentences with various topics .
A practical goal for natural language text generation research is to converge on a separation of functions into modules that can be independently re-used . This paper addresses issues related to software re-use and evolution in text generation systems . We describe the benefits we obtained by adapting and generalizing the generation modules and techniques we used for the successive development of three distinct text generation applications , PLANDoc , FLOWDoc , and ZEDDoc .
We are currently implementing CIRCSIM-Tutor v. 3 , a natural-language based intelligent tutoring system ( ITS ) which tutors medical students on a topic in cardiovascular physiology ( the baroreceptor reflex ) . We outline the text generation process in CIRCSIM-Tutor and illustrate a number of issues we have faced with respect to the representation of communicative action .
For that reason , spelling correction is needed to solve any writing mistakes . This design process discuss about the making of spelling correction for document text in Indonesian language with document 's text as its input and a .txt file as its output . For the realization , 5 000 news articles have been used as training data .
Spelling error correction is a Natural Language Processing ( NLP ) problem , and it has recently become relevant because many of the potential NLP applications such as text summarization , sentiment analysis and machine translation etc take advantage of spelling error analysis . Spell checking is a well-known task in NLP .
During UVL analysis we deal with domain-speci c terms in the input and we also support spelling error correction of those terms . Besides this , we apply the following three adaptive techniques : ( 1 ) the use of the Japanese-English dictionary EDICT as secondary lexicon for the retrieval of information about unknown words and the automatic generation of new lexical entries , ( 2 ) the semi-automatic extraction of domain-speci c terms from domain-speci c documents , and ( 3 ) the application of pronunciation data from the Japanese character le KANJIDIC to improve the precision of spelling error correction . The semantic component generates the semantic representation of the input sentence .
Similarly , OCR output contains almost exclusively substitution errors , which ordinarily account for less than 20 percent of keyboarded misspellings [ 9 , 10 ] . This paper describes the correction pro.gram developed as part of SPEEDCOP ( Spelling Error . Detection/ © 1984ACM0001 - 0782/84/0400 - 0358 75¢ Correction Project ) , a Chemical Abstracts Service ( CAS ) project supported by the National Science Foundation .
One kind of such preprocessing is automatic word spacing . This process decides correct boundaries between words in a sentence containing spacing errors , which are a type of spelling error . Except for some Asian languages such as Chinese and Japanese , most languages have explicit word spacing .
We read James L. Peterson 's article , “ Computer Programs for Detecting and Correcting Spelling Errors ” with great interest . We too have recently developed a spelling correction program as part of a tutorial on programming techniques and style . Our basic modesty inhibited us from publishing the details — there was , after all , little research involved in making the program .
Our spelling correction program is part of a system for understanding illformed input in an intelligent tutoring system for medical students . Speed and user-friendliness were the most important considerations in the design .
In Chinese , there are many characters which are similar in shape , and this phenomenon usually induces writing errors . As one important issue in spelling automatic correction , shape similarity measurement is still a challenging problem . To address this issue , we propose a component-tree based method in this paper , which is based on the hypothesis " characters are similar if their construction and components are both similar " .
It proposes interaction elements to support functionalities that assists user during query formulation . These functionalities include domain filter suggestion , query suggestion , and spelling correction functions .
Tamil language has an agglutinative , diglossic , alpha-syllabary structure which provides a significant combinatorial explosion of morphological forms all of which are effectively used in Tamil prose , poetry from antiquity to the modern age in an unbroken chain of continuity . However , for the language understanding , spelling correction purposes some of these present challenges as out-of-dictionary words . In this paper the authors propose algorithmic techniques to handle specific problems of conjoined-words ( out-of-dictionary ) (transliteration)[thendRalkattRu ] = [thendRal]+[kattRu ] when parts are alone present in word-list in efficient way .
We refer to this method as REINFORCE - critic . subsection : Spelling Correction We use text from the One Billion Word dataset for the spelling correction task chelba2013one , which has pre - defined training and testing sets . The training data was abundant , and we never used any example twice .
Online news and social media are transforming the process of news production and reading . While research has shown that news media play an important role in providing information to the public in democratic societies , research investigating the impact of sharing news online on the process of public opinion formation is in a nascent stage .
Social media monitoring is now an essential part of brand management , political science , and news production . Automatic tweet ranking and content recommendation methods are required , in order to support human analysts in deriving useful insights from large-scale social media data .
It implies many possible combinations of formerly discrete realms — print , television , radio and online media — and has an impact on the organisation of newsrooms with joint newsrooms for all channels and journalists working for more than one platform . Furthermore convergence changes the storytelling in journalism , because the Internet is a central hub of text , imagery , video and audio and is not only a content platform but also a distribution platform for conventional media — with active users taking part at the news process . Also the far-reaching consequences for the job profile and the education of journalists are discussed in this article .
Journalists retrieve information from a complex range of sources . Addresses the information sources which are used in the news process . Examines the preference for human sources as opposed to library‐based information and discusses the influence of pragmatic constraints like time and space on the production of news .
The thinking creation in news edition is the presupposition of the creation of news report . The thinking creation in news creation alone can be the creation of forms and contents in news report .
As the exponential growth of news creation on the internet , the amount of digital news has reached out billion numbers . Digital news is naturally linked each other but it needs to be grouped so that user can easily classify the news that they read .
Automatic Document Summarisation plays a central role in the process of providing the user with a quick access to information . Applications range from the generation of news headlines , to the aggregation of opinions extracted from reviews . Traditional topic-based summarisation systems are not always able to capture the sentiments expressed in a review .
Personalized Recommendation System Based on PRML for E-Commerce.- An Efficient Index Scheme for XML Databases.- On Separating Constant from Polynomial Ambiguity of Finite Automata.- Reliable Broadcasting Without Collision Detection.- Semi-strong Static Type Checking of Object-Oriented Query Languages.- Building a Fuzzy Transformation System.- News Generating Via Fuzzy Summarization of Databases.- Improving Web Sites with Web Usage Mining , Web Content Mining , and Semantic Analysis.- Automatic Distribution of Sequential Code Using JavaSymphony Middleware.- Unifying Framework for Message Passing.- Heuristics on the Definition of UML Refinement Patterns.- The Complexity of Problems on Implicitly Represented Inputs.-
An Improved News Generating . The paper introduces an improved method of intelligent summarization of large datasets .
We performed a second analysis by comparing the quality of text expansion in English to Spanish , using an ad-hoc Spanish-English parallel corpus . The system might also be applied to other domains such as report and news generation .
Therefore , we design agent-based model of opinion dynamics in social media that links information consumption and the formation of echo chamber . The model focuses on users interaction with news and the subsequent dynamics of echo chamber formation by presenting different cases of news generation and users behavior in SNS . The result is presented as suggestions to reduce the presence of echo chamber in SNS .
This workshop wanted to stress the vision of this powerful communication channel as a social sensor , which can be used to detect and characterize interesting and yet unreported information and events in real time , crossing all topics and locations . Future technologies on this connectivity may also provide applications with automatic techniques for the generation of news ( filtered over user profiles ) , offering a sideways to the existing authoritative information media .
It is the human that is the initial target , as well as the first line of defense . At the same time , social media has become a dominant , direct , and highly effective form of news generation and sharing at a global scale , in a manner that influences and enhances , but also challenges and often antagonizes , traditional media corporations .
Document summarization is of great value to many real world applications , such as snippets generation for search results and news headlines generation . Traditionally , document summarization is implemented by extracting sentences that cover the main topics of a document with a minimum redundancy .
The case study is the Brazilian politics scenario starting at the 2018 general elections on Twitter and WhatsApp . These two platforms were a fertile ground to fake news proliferation . We also evaluated the models over another available dataset from literature .
In recent years , with the development of deep learning technology , text generation technology has undergone great changes and provided many kinds of services for human beings , such as restaurant reservation and daily communication . The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text generation technology , that is the conditional text generation , including emotional text generation , personalized text generation , and so on . Conditional text generation ( c-TextGen ) has thus become a research hotspot .
In this paper , we focus on addressing the limitations caused by having to specify blanks to be filled . We decompose conditional text generation problem into two tasks , make-a-blank and fill-in-the-blank , and extend the former to handle more complex manipulations on the given tokens . We cast these tasks as a hierarchical multi agent RL problem and introduce a conditional adversarial learning that allows the agents to reach a goal , producing realistic texts , in cooperative setting .
Generating and Modifying Images Based on Continual Linguistic Instruction . Conditional text-to-image generation is an active area of research , with many possible applications . Existing research has primarily focused on generating a single image from available conditioning information in one step .
In particular , a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during model inference . Experimental results show that our TGVAE outperforms alternative approaches on both unconditional and conditional text generation , which can generate semantically-meaningful sentences with various topics .
Software Re-Use and Evolution in Text Generation Applications Representing Communicative Action in a Dialogue-Based Intelligent Tutoring System Spelling Correction for Text Documents in Bahasa Indonesia Using Finite State Automata and Levinshtein Distance Method Automatic Error Detection and Correction in Malayalam DOA - The Deductive Object-Oriented Approach to the Development of Adaptive Natural Language Interfaces (Abstract) Automatic spelling correction in scientific and scholarly text Automatic Word Spacing Using Probabilistic Models Based on Character n-grams Another spelling correction program Spelling Correction for an Intelligent Tutoring System Tree Based Shape Similarity Measurement for Chinese Characters Interactive interface for query formulation Algorithms for certain classes of Tamil Spelling correction An Actor-Critic Algorithm for Sequence Prediction Clicking vs. sharing: The relationship between online news behaviors and political knowledge A Human-annotated Dataset for Evaluating Tweet Ranking Algorithms Convergent Journalism—Newsrooms, Routines, Job Profiles and Training Journalistic construction of news: information gathering On the Thinking Creation of Editors in the Creation of News Report Digital News Graph Clustering using Chinese Whispers Algorithm A knowledge-based approach for summarising opinions SOFSEM 2006: Theory and Practice of Computer Science, 32nd Conference on Current Trends in Theory and Practice of Computer Science, Merín, Czech Republic, January 21-27, 2006, Proceedings Type-2 Fuzzy Summarization of Data: An Improved News Generating A System for Automatic English Text Expansion Agent-Based Approach to Echo Chamber Reduction Strategy in Social Media SIDEWAYS'19: 5th International Workshop on Social Media World Sensors Digital Deception: Cyber Fraud and Online Misinformation Document Summarization Based on Data Reconstruction Fake News Detection Using One-Class Classification c-TextGen: Conditional Text Generation for Harmonious Human-Machine Interaction Token Manipulation Generative Adversarial Network for Text Generation Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction Topic-Guided Variational Autoencoders for Text Generation
af52056c75fb11e6160390ecb312b780f88237fd 71524f8666b1dfccfd20ed282cafde5657fb7fe7 62487229cf3e53e49b6404bf5e8cce672fc209ed 0295d4cdc66161b17304d920a7bac7ad81f0e425 0482648736549ee159ed2a5f4ef7bc3defaf66d6 9267e23dcc108bfc21b949cd0ea95b7181a82570 f6c723cc78ae01310b61b65d1e41c60864e5f8ad 27cbbbbb2df69d10517cc7ef12e4e8109348bd9c a7169349f4bcfc3ce58fc5be01434eb226e8d470 42c53dad51a8ce5d25e07aee8e53a9d6129ad20f b055feefaec847dd8cae00571512adc48fcc9712 418f68c602f7dfb946ab9ca4127240a104289431 12a0dbbda4d512ec76a169637b0e955583b43c69 6d06bf42264287596ea635d4530aa68f01a9bdc7 1d7d7b8999c18c58f8f9d70b576d922c93db4b06 350aa75ecf450e6f2767bca1bd7ccb6070dc3cf4 4a483ed8102d1ac72df759c88ee8df44d237f9d5 8bc6e34542948aa716593005826a9323b7068d89 9bd189044a25d76f3d45335afe4d3d14713783c7 ed5cebf802209821a85febb01b9de619c6b43279 232706eeece2ee259626757225faca29062dbc3b bc60a7408abb697c2109bbf8b4848eff5340ac97 175b1963c0ede1caf98fef68f6c7abc2bd787e6c 4238f5374702059a41cb4a7163ec6d10693589f2 278ba1200dd03bd7b00ac57a85f7d96802d4d3e5 d7c58e4f16504500329315e06eeba700c4b7abca 0c77c6ec6268577d6cd53e307fa5187108e88b0a e324d92c005ccdec0ce04dfb9941dd99ded21920 aa2fa09b576411560ff3ab4ba1f9a56955682e2c 19f894ab29ed7895b9a0cc7435866da69e9a63c6 da487cccda0e5098ed8c3bfc5b7974914e628088
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Psychology"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"]
1997 1998 2018 2016 1997 1984 2007 1981 1989 2015 2013 2019 2017 2016 2015 2013 1997 2004 2017 2012 2006 2007 2019 2018 2019 2020 2012 2019 2019 2020 2018 2019
BNCOD Great Lakes Computer Science Conference KSEM OZCHI ICLR HT SIGIR RSEISP SCIS&amp;ISIS HT AAAI BRACIS
https://api.semanticscholar.org/af52056c75fb11e6160390ecb312b780f88237fd https://api.semanticscholar.org/71524f8666b1dfccfd20ed282cafde5657fb7fe7 https://api.semanticscholar.org/62487229cf3e53e49b6404bf5e8cce672fc209ed https://api.semanticscholar.org/0295d4cdc66161b17304d920a7bac7ad81f0e425 https://api.semanticscholar.org/0482648736549ee159ed2a5f4ef7bc3defaf66d6 https://api.semanticscholar.org/9267e23dcc108bfc21b949cd0ea95b7181a82570 https://api.semanticscholar.org/f6c723cc78ae01310b61b65d1e41c60864e5f8ad https://api.semanticscholar.org/27cbbbbb2df69d10517cc7ef12e4e8109348bd9c https://api.semanticscholar.org/a7169349f4bcfc3ce58fc5be01434eb226e8d470 https://api.semanticscholar.org/42c53dad51a8ce5d25e07aee8e53a9d6129ad20f https://api.semanticscholar.org/b055feefaec847dd8cae00571512adc48fcc9712 https://api.semanticscholar.org/418f68c602f7dfb946ab9ca4127240a104289431 https://www.semanticscholar.org/paper/0d24a0695c9fc669e643bad51d4e14f056329dec https://api.semanticscholar.org/12a0dbbda4d512ec76a169637b0e955583b43c69 https://api.semanticscholar.org/6d06bf42264287596ea635d4530aa68f01a9bdc7 https://api.semanticscholar.org/1d7d7b8999c18c58f8f9d70b576d922c93db4b06 https://api.semanticscholar.org/350aa75ecf450e6f2767bca1bd7ccb6070dc3cf4 https://api.semanticscholar.org/4a483ed8102d1ac72df759c88ee8df44d237f9d5 https://api.semanticscholar.org/8bc6e34542948aa716593005826a9323b7068d89 https://api.semanticscholar.org/9bd189044a25d76f3d45335afe4d3d14713783c7 https://api.semanticscholar.org/ed5cebf802209821a85febb01b9de619c6b43279 https://api.semanticscholar.org/232706eeece2ee259626757225faca29062dbc3b https://api.semanticscholar.org/bc60a7408abb697c2109bbf8b4848eff5340ac97 https://api.semanticscholar.org/175b1963c0ede1caf98fef68f6c7abc2bd787e6c https://api.semanticscholar.org/4238f5374702059a41cb4a7163ec6d10693589f2 https://api.semanticscholar.org/278ba1200dd03bd7b00ac57a85f7d96802d4d3e5 https://api.semanticscholar.org/d7c58e4f16504500329315e06eeba700c4b7abca https://api.semanticscholar.org/0c77c6ec6268577d6cd53e307fa5187108e88b0a https://api.semanticscholar.org/e324d92c005ccdec0ce04dfb9941dd99ded21920 https://api.semanticscholar.org/aa2fa09b576411560ff3ab4ba1f9a56955682e2c https://api.semanticscholar.org/19f894ab29ed7895b9a0cc7435866da69e9a63c6 https://api.semanticscholar.org/da487cccda0e5098ed8c3bfc5b7974914e628088