J. Lang. Technol. Comput. Linguistics最新文献_第3页

Document-level school lesson quality classification based on German transcripts 基于德语成绩单的文件级学校课程质量分类

J. Lang. Technol. Comput. Linguistics Pub Date : 2015-07-01 DOI: 10.21248/jlcl.30.2015.197

Lucie Flekova, Tahir Sousa, Margot Mieskes, Iryna Gurevych

{"title":"Document-level school lesson quality classification based on German transcripts","authors":"Lucie Flekova, Tahir Sousa, Margot Mieskes, Iryna Gurevych","doi":"10.21248/jlcl.30.2015.197","DOIUrl":"https://doi.org/10.21248/jlcl.30.2015.197","url":null,"abstract":"Analyzing large-bodies of audiovisual information with respect to discoursepragmatic categories is a time-consuming, manual activity, yet of growing importance in a wide variety of domains. Given the transcription of the audiovisual recordings, we propose to model the task of assigning discoursepragmatic categories as supervised machine learning task. By analyzing the effects of a wide variety of feature classes, we can trace back the discoursepragmatic ratings to low-level language phenomena and better understand their dependency. The major contribution of this article is thus a rich feature set to analyze the relationship between the language and the discoursepragmatic categories assigned to an analyzed audiovisual unit. As one particular application of our methodology, we focus on modelling the quality of lessons according to a set of discourse-pragmatic dimensions. We examine multiple lesson quality dimensions relevant for educational researchers, e.g. to which extent teachers provide objective feedback, encourage cooperation and pursue thinking pathways of students. Using the transcripts of real classroom interactions recorded in Germany and Switzerland, we identify a wide range of lexical, stylistic and discourse-pragmatic phenomena, which affect the perception of lesson quality, and we interpret our findings together with the educational experts. Our results show that especially features focusing on discourse and cognitive processes are beneficial for this novel classification task, and that this task has a high potential for automated assistance.","PeriodicalId":402489,"journal":{"name":"J. Lang. Technol. Comput. Linguistics","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125566781","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: 2

A relational database model and prototype for storing diverse discrete linguistic data 一种用于存储各种离散语言数据的关系数据库模型和原型

J. Lang. Technol. Comput. Linguistics Pub Date : 2015-07-01 DOI: 10.21248/jlcl.30.2015.194

Alexander Magidow

{"title":"A relational database model and prototype for storing diverse discrete linguistic data","authors":"Alexander Magidow","doi":"10.21248/jlcl.30.2015.194","DOIUrl":"https://doi.org/10.21248/jlcl.30.2015.194","url":null,"abstract":"This article describes a model for storing multiple forms of linguistic data within a relational database as developed and tested through a prototype database for storing data from Arabic dialects. A challenge that typically confronts linguistic documentation projects is the need for a flexible data model that can be adapted to the growing needs of a project (Dimitriadis, 2006). Contributors to linguistic databases typically cannot predict exactly which attributes of their data they will need to store, and therefore the initial design of the database may need to change over time. Many projects take advantage of the flexibility of XML and RDF to allow for continuing revisions to the data model. For some projects, there may be a compelling need to use a relational database system, though some approaches to relational database design may not flexible enough to allow for adaptation over time (Dimitriadis, 2006). The goal of this article is to describe a relational database model which can adapt easily to storing new data types as a project evolves. It both describes a general data model and shows its implementation within a working project. The model is primarily intended for storing discrete linguistic elements (phonemes, morphemes including general lexical data, sentences) as opposed to text corpora, and would be expected to store data on the order of thousands to hundreds of thousands of rows.1 The relational model described in this paper is centered around the linguistic datum, encoded as a string of characters, associated in a many-to-many relationship with ‘tags,’ and in many-to-many named relationships with other datums.2 For this reason, the model will be referred to as the ‘tag-and-relationship’ model. The combination of tags and relationships allows the database to store a wide variety of linguistic data. This data model was developed in tandem with a project to encode linguistic data from Arabic dialects (the “Database of Arabic Dialects”, DAD).3 Arabic is an extremely diverse language group, with a dialects stretching from Mauritania to Afghanistan,","PeriodicalId":402489,"journal":{"name":"J. Lang. Technol. Comput. Linguistics","volume":"27 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113974572","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

Discourse Segmentation of German Texts 德语语篇的语篇分割

J. Lang. Technol. Comput. Linguistics Pub Date : 2015-07-01 DOI: 10.21248/jlcl.30.2015.196

Wladimir Sidorenko, A. Peldszus, Manfred Stede

引用次数: 16

Sentiment Classification at Discourse Segment Level: Experiments on multi-domain Arabic corpus 语段层面的情感分类:多领域阿拉伯语语料库实验

J. Lang. Technol. Comput. Linguistics Pub Date : 2015-07-01 DOI: 10.21248/jlcl.30.2015.193

Amine Bayoudhi, Hatem Ghorbel, Houssem Koubaa, Lamia Hadrich Belguith

引用次数: 9

Building Linguistic Corpora from Wikipedia Articles and Discussions 从维基百科文章和讨论建立语言语料库

J. Lang. Technol. Comput. Linguistics Pub Date : 2014-07-01 DOI: 10.21248/jlcl.29.2014.189

Eliza Margaretha, H. Lüngen

引用次数: 36

IGGSA-STEPS: Shared Task on Source and Target Extraction from Political Speeches IGGSA-STEPS:从政治演讲中提取源和目标的共享任务

J. Lang. Technol. Comput. Linguistics Pub Date : 2014-07-01 DOI: 10.21248/jlcl.29.2014.182

Josef Ruppenhofer, Julia Maria Struß, J. Sonntag, Stefan Gindl

引用次数: 8

Challenges and experiences in collecting a chat corpus 收集聊天语料库的挑战与经验

J. Lang. Technol. Comput. Linguistics Pub Date : 2014-07-01 DOI: 10.21248/jlcl.29.2014.190

W. Spooren, T. V. Charldorp

引用次数: 2

Domain Adaptation for Opinion Mining: A Study of Multipolarity Words 面向意见挖掘的领域自适应:多极词研究

J. Lang. Technol. Comput. Linguistics Pub Date : 2014-07-01 DOI: 10.21248/jlcl.29.2014.181

M. Marchand, Romaric Besançon, O. Mesnard, Anne Vilnat

引用次数: 3

Unsupervised feature learning for sentiment classification of short documents 短文档情感分类的无监督特征学习

J. Lang. Technol. Comput. Linguistics Pub Date : 2014-07-01 DOI: 10.21248/jlcl.29.2014.180

S. Albertini, Alessandro Zamberletti, I. Gallo

引用次数: 6

Using Brain Data for Sentiment Analysis 使用大脑数据进行情感分析

J. Lang. Technol. Comput. Linguistics Pub Date : 2014-07-01 DOI: 10.21248/jlcl.29.2014.185

Yuqiao Gu, Fabio Celli, J. Steinberger, A. Anderson, Massimo Poesio, C. Strapparava, B. Murphy

引用次数: 7