Journal for Language Technology and Computational Linguistics最新文献_第2页

Empirische Verortung konzeptioneller Nähe/Mündlichkeit inner- und außerhalb schriftsprachlicher Korpora 在没有语言社团的地方对概念上接近/文谈进行实证跟踪

Journal for Language Technology and Computational Linguistics Pub Date : 2023-05-15 DOI: 10.21248/jlcl.36.2023.240

Sarah Broll, Romana Schneider

{"title":"Empirische Verortung konzeptioneller Nähe/Mündlichkeit inner- und außerhalb schriftsprachlicher Korpora","authors":"Sarah Broll, Romana Schneider","doi":"10.21248/jlcl.36.2023.240","DOIUrl":"https://doi.org/10.21248/jlcl.36.2023.240","url":null,"abstract":"Linguistische Studien arbeiten häufig mit einer Differenzierung zwischen gesprochener und geschriebener Sprache bzw. zwischen Kommunikation der Nähe und Distanz. Die Annahme eines Kontinuums zwischen diesen Polen bietet sich für eine Verortungunterschiedlichster Äußerungsformen an, inklusive unkonventioneller Textsorten wie etwa Popsongs. Wir konzipieren, implementieren und evaluieren ein automatisiertes Verfahren, das mithilfe unkorrelierter Entscheidungsbäume entsprechende Vorhersagenauf Textebene durchführt. Für die Identifizierung der Pole definieren wir einen Merkmalskatalog aus Sprachphänomenen, die als Markierer für Nähe/Mündlichkeit bzw. Distanz/Schriftlichkeit diskutiert werden, und wenden diesen auf prototypische Nähe-/Mündlichkeitstexte sowie prototypische Distanz-/Schrifttexte an. Basierend auf der sehr guten Klassifikationsgüte verorten wir anschließend eine Reihe weiterer Textsorten mithilfe der trainierten Klassifikatoren. Dabei erscheinen Popsongs als „mittige Textsorte“, die linguistisch motivierte Merkmale unterschiedlicher Kontinuumsstufen vereint. Weiterhin weisen wir nach, dass unsere Modelle mündlich kommunizierte, aber vorab oder nachträglich verschriftlichte Äußerungen wie Reden oder Interviews vollkommenanders verorten als prototypische Gesprächsdaten und decken Klassifikationsunterschiede für Social-Media-Varianten auf. Ziel ist dabei nicht eine systematisch-verbindliche Einordung im Kontinuum, sondern eine empirische Annäherung an die Frage, welchemaschinell vergleichsweise einfach bestimmbaren Merkmale („shallow features“) nachweisbar Einfluss auf die Verortung haben.","PeriodicalId":137584,"journal":{"name":"Journal for Language Technology and Computational Linguistics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125231883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts 任意OCR-ed历史文本自动后校正模型的优化训练

Journal for Language Technology and Computational Linguistics Pub Date : 2022-12-03 DOI: 10.21248/jlcl.35.2022.232

Tobias Englmeier, F. Fink, U. Springmann, K. Schulz

{"title":"Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts","authors":"Tobias Englmeier, F. Fink, U. Springmann, K. Schulz","doi":"10.21248/jlcl.35.2022.232","DOIUrl":"https://doi.org/10.21248/jlcl.35.2022.232","url":null,"abstract":"Systems for post-correction of OCR-results for historical texts are based on statistical correction models obtained by supervised learning. For training, suitable collections of ground truth materials are needed. In this paper we investigate the dependency of the power of automated OCR post-correction on the form of ground truth data and other training settings used for the computation of a post-correction model. The post-correction system A-PoCoTo considered here is based on a profiler service that computes a statistical profile for an OCR-ed input text. We also look in detail at the influence of the profiler resources and other settings selected for training and evaluation. As a practical result of several fine-tuning steps, a general post-correction model is achieved where experiments for a large and heterogeneous collection of OCR-ed historical texts show a consistent improvement of base OCR accuracy. The results presented are meant to provide insights for libraries that want to apply OCR post-correction to a larger spectrum of distinct OCR-ed historical printings and ask for \"representative\" results.","PeriodicalId":137584,"journal":{"name":"Journal for Language Technology and Computational Linguistics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127752939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Frame Detection in German Political Discourses: How Far Can We Go Without Large-Scale Manual Corpus Annotation? 德国政治话语中的框架检测:没有大规模人工语料库标注，我们还能走多远?

Journal for Language Technology and Computational Linguistics Pub Date : 2022-07-01 DOI: 10.21248/jlcl.35.2022.227

Qi Yu, Anselm Fliethmann

{"title":"Frame Detection in German Political Discourses: How Far Can We Go Without Large-Scale Manual Corpus Annotation?","authors":"Qi Yu, Anselm Fliethmann","doi":"10.21248/jlcl.35.2022.227","DOIUrl":"https://doi.org/10.21248/jlcl.35.2022.227","url":null,"abstract":"Automated detection of frames in political discourses has gained increasing attention in natural language processing (NLP). Earlier studies in this area however focus heavily on frame detection in English using supervised machine learning approaches. Addressing the difﬁculty of the lack of annotated data for training and/or evaluating supervised models for low-resource languages, we investigate the potential of two NLP approaches that do not require large-scale manual corpus annotation from scratch: 1) LDA-based topic modelling, and 2) a combination of word2vec embeddings and handcrafted framing keywords based on a novel, expert-curated framing schema. We test these approaches using a novel corpus consisting of German-language news articles on the “Eu-ropean Refugee Crisis” between 2014-2018. We show that while topic modelling is insufﬁcient in detecting frames in a dataset with highly homogeneous vocabulary, our second approach yields intriguing and more humanly interpretable results. This approach offers a promising opportunity to incorporate domain knowledge from political science and NLP techniques for bottom-up, explorative political text analyses.","PeriodicalId":137584,"journal":{"name":"Journal for Language Technology and Computational Linguistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124712135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Share and Shout: Proto-Slogans in Online Political Communities 分享与呐喊:网络政治社区的原始口号

Journal for Language Technology and Computational Linguistics Pub Date : 2022-07-01 DOI: 10.21248/jlcl.35.2022.228

Irene Russo, G. Comandini, T. Caselli, V. Patti

引用次数: 0

UNSC-NE: A Named Entity Extension to the UN Security Council Debates Corpus UNSC-NE:联合国安理会辩论语料库的具名实体扩展

Journal for Language Technology and Computational Linguistics Pub Date : 2022-07-01 DOI: 10.21248/jlcl.35.2022.229

Luis Glaser, R. Patz, Manfred Stede

引用次数: 2

Explaining Offensive Language Detection 解释攻击性语言检测

Journal for Language Technology and Computational Linguistics Pub Date : 2020-07-01 DOI: 10.21248/jlcl.34.2020.223

Julian Risch, Robert S. Ruff, Ralf Krestel

引用次数: 3

COLD: Annotation scheme and evaluation data set for complex offensive language in English 英语复杂攻击性语言标注方案与评价数据集

Journal for Language Technology and Computational Linguistics Pub Date : 2020-07-01 DOI: 10.21248/jlcl.34.2020.222

Alexis Palmer, Christine Carr, Melissa Robinson, Jordan Sanders

{"title":"COLD: Annotation scheme and evaluation data set for complex offensive language in English","authors":"Alexis Palmer, Christine Carr, Melissa Robinson, Jordan Sanders","doi":"10.21248/jlcl.34.2020.222","DOIUrl":"https://doi.org/10.21248/jlcl.34.2020.222","url":null,"abstract":"This paper presents a new, extensible annotation scheme for oﬀensive language data sets. The annotation scheme expands coverage beyond fairly straightforward cases of oﬀensive language to address several cases of complex, implicit, and/or pragmatically-triggered oﬀensive language. We apply the annotation scheme to create a new Complex Oﬀensive Language Data Set for English ( COLD-EN ). The primary purpose of this data set is to diagnose how well systems for automatic detection of abusive language are able to classify three types of complex oﬀensive language: reclaimed slurs, oﬀensive utterances containing pejorative adjectival nominalizations (and no slur terms), and utterances conveying oﬀense through linguistic distancing. COLD oﬀers a straightforward framework for error analysis. Our vision is that researchers will use this data set to diagnose the strengths and weaknesses of their oﬀensive language detection systems. In this paper, we diagnose some strengths and weaknesses of a top-performing oﬀensive language detection system by: a) using it to classify COLD , and b) investigating its performance on the 10 ﬁne-grained categories supported by our annotation scheme. We evaluate the system’s performance when trained on ﬁve diﬀerent standard data sets for oﬀensive language detection. Systems trained on diﬀerent data sets have diﬀerent strengths and weaknesses, with most performing poorly on the phenomena of reclaimed slurs and pejorative nominalizations. NOTE: This paper contains sensitive and oﬀensive material. The oﬀensive materials are part of a complex puzzle we wish to better understand; they appear in the form of lightly-censored slurs and degrading insults. We do not condone this type of language, nor does it reﬂect the attitudes or beliefs of the authors.","PeriodicalId":137584,"journal":{"name":"Journal for Language Technology and Computational Linguistics","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127327801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Automating News Comment Moderation with Limited Resources: Benchmarking in Croatian and Estonian 自动化新闻评论审核与有限的资源:在克罗地亚和爱沙尼亚的基准

Journal for Language Technology and Computational Linguistics Pub Date : 2020-07-01 DOI: 10.21248/jlcl.34.2020.224

Ravi Shekhar, M. Pranjic, S. Pollak, Andraz Pelicon, Matthew Purver

引用次数: 11

Text Segmentation with Topic Models 主题模型的文本分割

Journal for Language Technology and Computational Linguistics Pub Date : 2012-07-01 DOI: 10.21248/jlcl.27.2012.158

Martin Riedl, Chris Biemann

引用次数: 61

E-Learning and Computational Linguistics An Introduction 电子学习与计算语言学导论

Journal for Language Technology and Computational Linguistics Pub Date : 2011-07-01 DOI: 10.21248/jlcl.26.2011.132

Maja Bärenfänger, Maik Stührenberg

引用次数: 0