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Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature
2017
Computing and informatics
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This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. ...
Natural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on nontextual data or from meaning representations. ...
Acknowledgements This material is based on a study funded by the School of Computer and Mathematical Sciences, Auckland University of Technology to build a Natural Language Generation framework. ...
doi:10.4149/cai_2017_1_1
fatcat:sxuxcvjb2rcq5habjgq6stvn3i
Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies
2021
Entropy
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As the ultimate purpose of SCG, Natural Language-based Source Code Generation (NLSCG) is growing into an attractive and challenging field, as the expressibility and extremely high abstraction of the input ...
There is no systematic study to explore and promote the further development of this field. We carried out a systematic literature survey and tool research to find potential improvement directions. ...
(2) We clarify the NLSCG field via an elaborate literature survey and get a distinct problem understanding. ...
doi:10.3390/e23091174
pmid:34573800
fatcat:vggfycedirff5eqb3ddogtnbry
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
[article]
2021
arXiv
pre-print
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We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches. ...
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. ...
We provide a survey of how recent studies have addressed a varied set of NLP applications. Text Classification. ...
arXiv:2111.01243v1
fatcat:4xfjkkby2bfnhdrhmrdlliy76m
Recent advances in methods of lexical semantic relatedness – a survey
2012
Natural Language Engineering
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Further, due to the polysemous nature of natural language, name references are often ambiguous. ...
It is recognised that a fundamental task in Information Extraction is Named Entity Recognition, the goals of which are identifying references of named entities in unstructured documents, and classifying ...
Also due to the similar nature of the two tasks, a large number of empirical methods of NER and NED are also built on certain common grounds. ...
doi:10.1017/s1351324912000125
fatcat:b62qbqwrqfaf3gytw22yktc5ae
A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances
2017
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
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In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. ...
A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. ...
This work is partially supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000904. ...
doi:10.18653/v1/d17-1123
dblp:conf/emnlp/WangHZ17
fatcat:onmgzexqubbw7ixavokbrv2l64
Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data
2019
IMIA Yearbook of Medical Informatics
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Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public ...
Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ...
doi:10.1055/s-0039-1677918
pmid:31419834
pmcid:PMC6697505
fatcat:r5ubjbhoszh47flxg6tf7n5eku
Generic Generalizations in Science
2018
Erkenntnis: An International Journal of Scientific Philosophy
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The last section indicates ways in which this extension is fruitful for the two strands of research that we combine: the philosophy of science literature on generalizations and the semantics literature ...
One argument is that the interpretation in terms of ceteris paribus laws is a historical accident. ...
A Classification of generalizations in the sciences In this section, we develop a classification of scientific generalizations. ...
doi:10.1007/s10670-018-9983-x
fatcat:bcrweb6q7jfuxlbsfyibzhlkde
A Survey on Modeling Language Evolution in the New Millennium
2019
New generation computing
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Therefore, modeling language evolution has attracted the interest of several researchers giving rise to a lot of models in the literature of the last millennium. ...
Language is a complex evolving system and it is not a trivial task to model the dynamics of processes occurring during its evolution. ...
These models have been surveyed by several authors [21, 23, 27, 55] in the literature. ...
doi:10.1007/s00354-019-00079-7
fatcat:2vezwkkhdbfpnhuljfpsvo5tg4
Generic adaptable test cases for software product line testing
2012
Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity - SPLASH '12
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Our work focuses on effort reduction via systematic reuse of generic test assets by taking advantage of common aspects and predicted variability in test cases. ...
We envision that the proposed approach to organizing test case libraries will be particularly useful in the context of Software Product Line Testing (SPLT). ...
The study also reveals a trend in increase of test automation recently. RQ3: How is generic test artifacts managed at different levels and phases? ...
doi:10.1145/2384716.2384733
dblp:conf/oopsla/AsaithambiJ12
fatcat:5i7jhdj3xvhq3h7g7aqow3ptnu
Progress in General Systems Research
[chapter]
1978
Applied General Systems Research
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There is still a gulf between the theory and natural language, but the advances of the past twenty years have been very great and show no signs of slowing down. ...
No a priori classification of system quantities to input and output are needed. The classification must generally follow (if it is at all possible) from the other traits of the system. ...
doi:10.1007/978-1-4757-0555-3_1
fatcat:arman7rbk5ganh6lgirqy7vfri
Generate, Annotate, and Learn: NLP with Synthetic Text
[article]
2021
arXiv
pre-print
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We investigate the use of generative models in synthesizing unlabeled data and present a simple and general framework called "generate, annotate, and learn (GAL)". ...
A language model (LM) is used to synthesize in-domain unlabeled data. Then, a classifier is used to annotate such data. ...
In Proceedings of the 2019 Conference on Empirical Methods in Natural
Language Processing and the 9th International Joint Conference on Natural Language Process-
ing (EMNLP-IJCNLP), pp. 6383–6389, ...
arXiv:2106.06168v2
fatcat:wrwmtxtqozcu5bk4rouw7buxwm
Generalizing to Unseen Domains: A Survey on Domain Generalization
[article]
2022
arXiv
pre-print
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Great progress has been made in the area of domain generalization for years. This paper presents the first review of recent advances in this area. ...
Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years. ...
[3] also wrote a survey on DG, while their focus is in computer vision field. A more recent survey paper is on out-of-distribution (OOD) generalization by Shen et al. [4] . ...
arXiv:2103.03097v7
fatcat:ry4ggjl63bhlzdhg3gojvyk2v4
Neural Language Generation: Formulation, Methods, and Evaluation
[article]
2020
arXiv
pre-print
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In this survey we formally define and categorize the problem of natural language generation. ...
Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language. ...
Acknowledgments This work was in part supported by the National Science Foundation under grant number 1633370. ...
arXiv:2007.15780v1
fatcat:oixtreazxvbgvclicpxiqzbxrm
Generalizing from a Few Examples: A Survey on Few-Shot Learning
[article]
2020
arXiv
pre-print
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Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this paper, we conduct a thorough survey to fully understand FSL. ...
We then point out that the core issue in FSL is that the empirical risk minimized is unreliable. ...
ACKNOWLEDGMENTS This research is partially done in 4Paradigm Inc. when Yaqing Wang took the internship. ...
arXiv:1904.05046v3
fatcat:t3ipecry4vc2thzdu6sv65epwa
Characteristic studies of loop problems for structural test generation via symbolic execution
2013
2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)
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Then the study methodology continues with conducting an empirical study of applying the existing techniques on real-world software applications sampled based on the literature-survey results and major ...
However, there exists no literature-survey or empirical work that shows the pervasiveness of loop problems or identifies challenges faced by these techniques on real-world open-source applications. ...
We thank Patrice Godefroid for his valuable feedback on an early version of the work described in this paper. ...
doi:10.1109/ase.2013.6693084
dblp:conf/kbse/XiaoLXT13
fatcat:msru5e5ou5hlxpqc6aftok4zye
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