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Pre-trained Contextualized Character Embeddings Lead to Major Improvements in Time Normalization: a Detailed Analysis

Dongfang Xu, Egoitz Laparra, Steven Bethard
<span title="">2019</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/62lejef5rzghbafcrd6wj6pc7u" style="color: black;">Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*</a> </i> &nbsp;
We derive character-level contextual embeddings from Flair (Akbik et al., 2018), and apply them to a time normalization task, yielding major performance improvements over the previous state-of-the-art:  ...  We analyze the sources of these improvements, and find that pre-trained contextual character embeddings are more robust to term variations, infrequent terms, and cross-domain changes.  ...  The computations were done in systems supported by the National Science Foundation under Grant No. 1228509.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/s19-1008">doi:10.18653/v1/s19-1008</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/starsem/XuLB19.html">dblp:conf/starsem/XuLB19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zgewllgwbzgu5asb6p5f27ocx4">fatcat:zgewllgwbzgu5asb6p5f27ocx4</a> </span>
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Enhancing BERT for Lexical Normalization

Benjamin Muller, Benoit Sagot, Djamé Seddah
<span title="">2019</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h24erjyw5ngjnhyvkk46z3atqq" style="color: black;">Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</a> </i> &nbsp;
Language model-based pre-trained representations have become ubiquitous in natural language processing.  ...  They have been shown to significantly improve the performance of neural models on a great variety of tasks.  ...  Introduction Pre-trained contextual language models (e.g.  ... 
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Thai Spelling Correction and Word Normalization on Social Text using a Two-stage Pipeline with Neural Contextual Attention

Anuruth Lertpiya, Tawunrat Chalothorn, Ekapol Chuangsuwanich
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Our method consists of a neuralbased error detector and a Seq2Seq neural error corrector with contextual attention.  ...  In this paper, we investigated how current text correction systems perform on correcting errors and word variances in Thai social texts and propose a method designed for this task.  ...  The authors would like to thank the linguist at KLabs, Nutcha Tirasaroj, for her help with the error analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3010828">doi:10.1109/access.2020.3010828</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7mdoiniof5eahmm32w4beefmgq">fatcat:7mdoiniof5eahmm32w4beefmgq</a> </span>
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Re-ranking system with BERT for biomedical concept normalization

Hyejin Cho, Dongha Choi, Hyunju Lee
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Recent studies suggest further pre-training of a pre-trained LM with the in-domain data for task adaptation, and show improved performance and effectiveness on downstream tasks from each target domain  ...  Although it takes approximately 8.5 hours to pre-train our models regardless of the length of task-specific data, each epoch, during fine-tuning, takes a different time ranging from 10 minutes to 1.3 hours  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3108445">doi:10.1109/access.2021.3108445</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c4getjhvhbee3aggryzmakxbhi">fatcat:c4getjhvhbee3aggryzmakxbhi</a> </span>
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Neural Text Normalization for Turkish Social Media

Sinan Goker, Burcu Can
<span title="">2018</span> <i title="IEEE"> 2018 3rd International Conference on Computer Science and Engineering (UBMK) </i> &nbsp;
In this study, two neural approaches are applied for Turkish text normalization task: Contextual Normalization approach using distributed representations of words and Sequence-to-Sequence Normalization  ...  Social media has become a rich data source for natural language processing tasks with its worldwide use; however, it is hard to process social media data directly in language studies due to its unformatted  ...  One method makes use of pre-trained neural word embeddings in order to include the contextual information to capture the canonical form of a given word.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ubmk.2018.8566406">doi:10.1109/ubmk.2018.8566406</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p6uyax4tvnhwhbvjclssxwhj34">fatcat:p6uyax4tvnhwhbvjclssxwhj34</a> </span>
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An Approach for Buyer Name Normalization in Pharmacy Sales Data

Jiajing Li, Wang Jia, Fuhui Nie, Hongyan You, Yaxin Hao
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The three layers are described in detail below. A. BERT We use BERT as the embedding layer.  ...  words in it in it, and then feed to a logistic regression; TextCNN [3] consists of embedding layer, convolution layer, max pooling , FC and softmax layer; TextRNN [36] takes the pre-trained embedding  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3093028">doi:10.1109/access.2021.3093028</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2udr74qvb5hdbg6i74clam6h6y">fatcat:2udr74qvb5hdbg6i74clam6h6y</a> </span>
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Investigating of Disease Name Normalization Using Neural Network and Pre-training

Yinxia Lou, Tao Qian, Fei Li, Junxiang Zhou, Donghong Ji, Ming Cheng
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In addition, character information and pre-training techniques are helpful for this task while sentence information hurts the performance.  ...  Moreover, we proposed a novel approach for pretraining models, which improved the F1 by up to 9%.  ...  (ii) CNN and Attention network is superior to the LSTM network in this task. (iii) Both pre-training word embeddings and pre-training models can improve the performance significantly.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2992130">doi:10.1109/access.2020.2992130</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ow4wqxbutrcovkgp5q7abwmota">fatcat:ow4wqxbutrcovkgp5q7abwmota</a> </span>
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Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization [article]

Dongkyu Lee, Zhiliang Tian, Lanqing Xue, Nevin L. Zhang
<span title="2021-08-01">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style.  ...  Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content.  ...  The hourly pay was set to 100 Hong Kong dollars (HK$), which is higher than Hong Kong's statutory minimum wage.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.00449v1">arXiv:2108.00449v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pfkej3jad5dvpho2oimc7hsjgq">fatcat:pfkej3jad5dvpho2oimc7hsjgq</a> </span>
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Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning [article]

Shengqiong Wu and Hao Fei and Donghong Ji
<span title="2020-09-19">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we target improving the ALD by jointly performing text normalization (TN), via an adversarial multi-task learning framework.  ...  Further analysis is conducted for a better understanding of our method.  ...  We thus use a character-level CNN to generate sub-word embeddings for each word, denoted as v sbw t .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.09174v1">arXiv:2009.09174v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ddazr3eomjewrppuncyncmjqie">fatcat:ddazr3eomjewrppuncyncmjqie</a> </span>
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Data-Driven Lexical Normalization for Medical Social Media

Dirkson, Verberne, Sarker, Kraaij
<span title="2019-08-20">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bn3b3ia5mzg4nbdativq7ki3sy" style="color: black;">Multimodal Technologies and Interaction</a> </i> &nbsp;
In the medical domain, user-generated social media text is increasingly used as a valuablecomplementary knowledge source to scientific medical literature.  ...  We also present the first corpus for spelling mistake detectionand correction in a medical patient forum.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/mti3030060">doi:10.3390/mti3030060</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5gp7wxxqpbg3bbc5v2lsmqqbly">fatcat:5gp7wxxqpbg3bbc5v2lsmqqbly</a> </span>
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Lexical Normalization for Code-switched Data and its Effect on POS-tagging [article]

Rob van der Goot, Özlem Çetinoğlu
<span title="2021-01-31">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of manynatural language processing tasks on social media.  ...  Results show that our CS-tailored normalization models outperform Id-En state of the art and Tr-De monolingual models, and lead to 5.4% relative performance increase for POS tagging as compared to unnormalized  ...  Acknowledgements We would like to thank Barbara Plank, Alan Ramponi, Marija Stepanovic, and Agnieszka Falenska, for feedback on early drafts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.01175v2">arXiv:2006.01175v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x6oax6es5nhmvbixudolsnl3oy">fatcat:x6oax6es5nhmvbixudolsnl3oy</a> </span>
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Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task

<span title="2018-10-01">2018</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aapnwtybrvghlc35hfimbtdlom" style="color: black;">JAMIA Journal of the American Medical Informatics Association</a> </i> &nbsp;
and normalization of health-related text from social media.  ...  An additional objective was to publicly release manually annotated data.  ...  The authors would also like to thank the anonymous reviewers for their critiques and suggestions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/jamia/ocy114">doi:10.1093/jamia/ocy114</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30272184">pmid:30272184</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6188524/">pmcid:PMC6188524</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xcnb65ojmvbh7e55ylxhsnusrm">fatcat:xcnb65ojmvbh7e55ylxhsnusrm</a> </span>
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Filipino Teacher Professional Development in the New Normal

Inero Ancho, Philippine Normal University, Manila, Philippines, Gilbert Arrieta, Philippine Normal University, Manila, Philippines
<span title="2021-09-30">2021</span> <i title="Kazan Federal University"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ijost6gmzbcvpgz2osvexclrui" style="color: black;">Obrazovanie i Samorazvitie</a> </i> &nbsp;
Regardless of age and years of teaching experience, teachers have a mindset to grow in the profession and be better educators.  ...  The Covid-19 pandemic has posed various challenges particularly in the education sector where the 'new normal' experience is all about online interaction and distance learning.  ...  The detailed analysis begins with a coding process. Coding is the process of organizing the material into chunks or segments of text before bringing meaning to information.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.26907/esd.16.3.04">doi:10.26907/esd.16.3.04</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u4scf22uurgexg4f62wuime7vy">fatcat:u4scf22uurgexg4f62wuime7vy</a> </span>
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Normalized Statistical Algorithm for Afaan Oromo Word Sense Disambiguation

Abdo Ababor Abafogi, Department of Information Technology, College of Computing and Informatics, Wolkite University, Ethiopia
<span title="2021-12-08">2021</span> <i title="MECS Publisher"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/koacvjimprbbvhwasrewxdbiuy" style="color: black;">International Journal of Intelligent Systems and Applications</a> </i> &nbsp;
In this paper, I propose a normalized statistical algorithm that performs the task of WSD for Afaan Oromo language despite morphological analysis The propose algorithm has the power to discriminate ambiguous  ...  As the major aim of WSD is to accurately understand the sense of a word in particular context, can be used for the correct labeling of words in natural language applications.  ...  Improved Word Sense Disambiguation using pre-trained contextualized word representations. In Proc. Of EMNLP, pages 5297–5306, 2019. [11] Michele Bevilacqua and RobertoNavigli.  ... 
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Music Does Not Facilitate Lexical Tone Normalization: A Speech-Specific Perceptual Process

Ran Tao, Kaile Zhang, Gang Peng
<span title="2021-10-22">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5r5ojcju2repjbmmjeu5oyawti" style="color: black;">Frontiers in Psychology</a> </i> &nbsp;
In this study, we examined this confounding factor by recruiting a group of native Cantonese speakers with sufficient musical training experience and a control group with minimal musical training.  ...  Participants performed lexical tone judgment tasks in three contextual conditions, i.e., speech, non-speech, and music context conditions.  ...  Considering musicians' improved pitch perception ability, they are expected to extract the contextual pitch information more accurately and thus have a more precise pitch range reference to estimate the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fpsyg.2021.717110">doi:10.3389/fpsyg.2021.717110</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34777097">pmid:34777097</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8585521/">pmcid:PMC8585521</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gy6tzonix5hsfnrebrr7ohnnvy">fatcat:gy6tzonix5hsfnrebrr7ohnnvy</a> </span>
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