Special Interest Group on Computational Morphology and Phonology Workshop最新文献_第4页

Mining linguistic tone patterns with symbolic representation 用符号表示挖掘语言语气模式

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2001

Shuo Zhang

引用次数: 2

Towards robust cross-linguistic comparisons of phonological networks 走向健全的跨语言语音网络比较

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2018

Philippa Shoemark, S. Goldwater, James P. Kirby, Rik Sarkar

引用次数: 12

The SIGMORPHON 2016 Shared Task—Morphological Reinflection SIGMORPHON 2016共享任务-形态反射

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2002

Ryan Cotterell, Christo Kirov, John Sylak-Glassman, David Yarowsky, Jason Eisner, Mans Hulden

{"title":"The SIGMORPHON 2016 Shared Task—Morphological Reinflection","authors":"Ryan Cotterell, Christo Kirov, John Sylak-Glassman, David Yarowsky, Jason Eisner, Mans Hulden","doi":"10.18653/v1/W16-2002","DOIUrl":"https://doi.org/10.18653/v1/W16-2002","url":null,"abstract":"The 2016 SIGMORPHON Shared Task was devoted to the problem of morphological reinﬂection. It introduced morphological datasets for 10 languages with diverse ty-pological characteristics. The shared task drew submissions from 9 teams representing 11 institutions reﬂecting a variety of approaches to addressing supervised learning of reinﬂection. For the simplest task, in-ﬂection generation from lemmas, the best system averaged 95.56% exact-match accuracy across all languages, ranging from Maltese (88.99%) to Hungarian (99.30%). With the relatively large training datasets provided, recurrent neural network architectures consistently performed best—in fact, there was a signiﬁcant margin between neural and non-neural approaches. The best neural approach, averaged over all tasks and languages, outperformed the best non-neural one by 13.76% absolute; on individual tasks and languages the gap in accuracy sometimes exceeded 60%. Overall, the results show a strong state of the art, and serve as encouragement for future shared tasks that explore morphological analysis and generation with varying degrees of supervision.","PeriodicalId":186158,"journal":{"name":"Special Interest Group on Computational Morphology and Phonology Workshop","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116510257","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: 236

Read my points: Effect of animation type when speech-reading from EMA data 阅读我的观点:从EMA数据中阅读演讲时动画类型的影响

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2014

Kristy James, Martijn B. Wieling

引用次数: 1

The Columbia University - New York University Abu Dhabi SIGMORPHON 2016 Morphological Reinflection Shared Task Submission 哥伦比亚大学-阿布扎比纽约大学SIGMORPHON 2016形态学反思共享任务提交

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2011

Dima Taji, R. Eskander, Nizar Habash, Owen Rambow

引用次数: 10

Inferring Morphotactics from Interlinear Glossed Text: Combining Clustering and Precision Grammars 结合聚类和精确语法从行间有光泽文本推断形态策略

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2021

Olga Zamaraeva

{"title":"Inferring Morphotactics from Interlinear Glossed Text: Combining Clustering and Precision Grammars","authors":"Olga Zamaraeva","doi":"10.18653/v1/W16-2021","DOIUrl":"https://doi.org/10.18653/v1/W16-2021","url":null,"abstract":"In this paper I present a k-means clustering approach to inferring morphological position classes (morphotactics) from Interlinear Glossed Text (IGT), data collections available for some endangered and low-resource languages. While the experiment is not restricted to low-resource languages, they are meant to be the targeted domain. Specifically my approach is meant to be for field linguists who do not necessarily know how many position classes there are in the language they work with and what the position classes are, but have the expertise to evaluate different hypotheses. It builds on an existing approach (Wax, 2014), but replaces the core heuristic with a clustering algorithm. The results mainly illustrate two points. First, they are largely negative, which shows that the baseline algorithm (summarized in the paper) uses a very predictive feature to determine whether affixes belong to the same position class, namely edge overlap in the affix graph. At the same time, unlike the baseline method that relies entirely on a single feature, kmeans clustering can account for different features and helps discover more morphological phenomena, e.g. circumfixation. I conclude that unsupervised learning algorithms such as k-means clustering can in principle be used for morphotactics inference, though the algorithm should probably weigh certain features more than others. Most importantly, I conclude that clustering is a promising approach for diverse morphotactics and as such it can facilitate linguistic analysis of field languages.","PeriodicalId":186158,"journal":{"name":"Special Interest Group on Computational Morphology and Phonology Workshop","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115940751","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: 10

Morphological reinflection with conditional random fields and unsupervised features 具有条件随机场和无监督特征的形态反射

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2006

L. Liu, L. Mao

引用次数: 15

Predicting the Direction of Derivation in English Conversion 英语转换中派生方向的预测

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2015

M. Kisselew, Laura Rimell, Alexis Palmer, Sebastian Padó

引用次数: 29

EHU at the SIGMORPHON 2016 Shared Task. A Simple Proposal: Grapheme-to-Phoneme for Inflection EHU在SIGMORPHON 2016共享任务大会上。一个简单的建议:字母到音素的屈折

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2016-08-01 DOI: 10.18653/v1/W16-2004

I. Alegria, Izaskun Etxeberria

引用次数: 10

Three Correlates of the Typological Frequency of Quantity-Insensitive Stress Systems 数量不敏感应力系统类型频率的三个相关因素

Special Interest Group on Computational Morphology and Phonology Workshop Pub Date : 2008-06-19 DOI: 10.3115/1626324.1626330

Max Bane, Jason Riggle

引用次数: 17