Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037711
Yan Li, Zhiyi Wu
Values of the basic tones are the key to do research on dialects in China. The traditional method of determining tones by ear and the more popular method used in experimental phonetics are either inaccurate to some degree or difficult to learn. The method provided and discussed in this paper is simple and reliable, requiring the use of only Praat and fundamental frequency value. More examples are given to prove this method’s effectiveness.
{"title":"A New Method of Tonal Determination for Chinese Dialects","authors":"Yan Li, Zhiyi Wu","doi":"10.1109/IALP48816.2019.9037711","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037711","url":null,"abstract":"Values of the basic tones are the key to do research on dialects in China. The traditional method of determining tones by ear and the more popular method used in experimental phonetics are either inaccurate to some degree or difficult to learn. The method provided and discussed in this paper is simple and reliable, requiring the use of only Praat and fundamental frequency value. More examples are given to prove this method’s effectiveness.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"103 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123525775","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037662
Yanchun Zhang, Xingyuan Chen, Peng Jin, Yajun Du
The neural language models (NLMs), such as long short term memery networks (LSTMs), have achieved great success over the years. However the NLMs usually only minimize a loss between the prediction results and the target words. In fact, the context has natural diversity, i.e. there are few words that could occur more than once in a certain length of word sequence. We report the natural diversity as context’s diversity in this paper. The context’s diversity, in our model, means there is a high probability that the target words predicted by any two contexts are different given a fixed input sequence. Namely the softmax results of any two contexts should be diverse. Based on this observation, we propose a new cross-entropy loss function which is used to calculate the cross-entropy loss of the softmax outputs for any two different given contexts. Adding the new cross-entropy loss, our approach could explicitly consider the context’s diversity, therefore improving the model’s sensitivity of prediction for every context. Based on two typical LSTM models, one is regularized by dropout while the other is not, the results of our experiment show its effectiveness on the benchmark dataset.
{"title":"Exploring Context’s Diversity to Improve Neural Language Model","authors":"Yanchun Zhang, Xingyuan Chen, Peng Jin, Yajun Du","doi":"10.1109/IALP48816.2019.9037662","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037662","url":null,"abstract":"The neural language models (NLMs), such as long short term memery networks (LSTMs), have achieved great success over the years. However the NLMs usually only minimize a loss between the prediction results and the target words. In fact, the context has natural diversity, i.e. there are few words that could occur more than once in a certain length of word sequence. We report the natural diversity as context’s diversity in this paper. The context’s diversity, in our model, means there is a high probability that the target words predicted by any two contexts are different given a fixed input sequence. Namely the softmax results of any two contexts should be diverse. Based on this observation, we propose a new cross-entropy loss function which is used to calculate the cross-entropy loss of the softmax outputs for any two different given contexts. Adding the new cross-entropy loss, our approach could explicitly consider the context’s diversity, therefore improving the model’s sensitivity of prediction for every context. Based on two typical LSTM models, one is regularized by dropout while the other is not, the results of our experiment show its effectiveness on the benchmark dataset.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124245898","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037703
Gulnur Arkin, Gvljan Alijan, A. Hamdulla, Mijit Ablimit
In this paper, based on the vowel and phonological pronunciation corpora of 20 Kazakh undergraduate Mandarin learners, 10 Uyghur learners, and 10 standard pronunciations, under the framework of the phonetic learning model and comparative analysis, the method of experimental phonetics will be applied to the Kazak and Uyghur learners. The learners and standard speaker Mandarin vowels were analyzed for acoustic characteristics, such as formant frequency values, the vowel duration similarity and other prosodic parameters were compared with the standard speaker. These results are conducive to providing learners with effective teaching-related reference information, providing reliable and correct parameters and pronunciation assessments for computer-assisted language teaching systems (CALLs), as well as improving the accuracy of multinational Chinese Putonghua speech recognition and ethnic identification.
{"title":"A Comparative Analysis of Acoustic Characteristics between Kazak & Uyghur Mandarin Learners and Standard Mandarin Speakers","authors":"Gulnur Arkin, Gvljan Alijan, A. Hamdulla, Mijit Ablimit","doi":"10.1109/IALP48816.2019.9037703","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037703","url":null,"abstract":"In this paper, based on the vowel and phonological pronunciation corpora of 20 Kazakh undergraduate Mandarin learners, 10 Uyghur learners, and 10 standard pronunciations, under the framework of the phonetic learning model and comparative analysis, the method of experimental phonetics will be applied to the Kazak and Uyghur learners. The learners and standard speaker Mandarin vowels were analyzed for acoustic characteristics, such as formant frequency values, the vowel duration similarity and other prosodic parameters were compared with the standard speaker. These results are conducive to providing learners with effective teaching-related reference information, providing reliable and correct parameters and pronunciation assessments for computer-assisted language teaching systems (CALLs), as well as improving the accuracy of multinational Chinese Putonghua speech recognition and ethnic identification.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"53 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130861347","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}
Differences of letter usage are the most basic differences between different languages, which can reflect the most essential diversity. Many linguists study the letter differences between common languages, but seldom research those between non-common languages. This paper selects three representative languages from the Indonesian branch of the Austronesian language family, namely Malay, Indonesian and Filipino. To study the letter differences between these three languages and English, we concentrate on word length distribution, letter frequency distribution, commonly used letter pairs, commonly used letter trigrams, and ranked letter frequency distribution. The results show that great differences do exist between three Indonesian-branch languages and English, and the differences between Malay and Indonesian are the smallest.
{"title":"Exploring Letter’s Differences between Partial Indonesian Branch Language and English","authors":"Nankai Lin, Sihui Fu, Jiawen Huang, Sheng-yi Jiang","doi":"10.1109/IALP48816.2019.9037715","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037715","url":null,"abstract":"Differences of letter usage are the most basic differences between different languages, which can reflect the most essential diversity. Many linguists study the letter differences between common languages, but seldom research those between non-common languages. This paper selects three representative languages from the Indonesian branch of the Austronesian language family, namely Malay, Indonesian and Filipino. To study the letter differences between these three languages and English, we concentrate on word length distribution, letter frequency distribution, commonly used letter pairs, commonly used letter trigrams, and ranked letter frequency distribution. The results show that great differences do exist between three Indonesian-branch languages and English, and the differences between Malay and Indonesian are the smallest.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712179","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037654
Huibin Zhuang, Zhanting Bu
In Chinese he 河 ‘river’ can be used as proper names (for the Yellow River), as well as a common word for rivers in North China. Based on linguistic data, ethnological evidence and historical documents, this paper argues against these leading hypotheses and proposes that he originated from the Old Yi language, entered Chinese through language contact, and replaced shui which was from Old Qiang and later became the only common noun for river in North China.
{"title":"On the Etymology of he ‘river’ in Chinese","authors":"Huibin Zhuang, Zhanting Bu","doi":"10.1109/IALP48816.2019.9037654","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037654","url":null,"abstract":"In Chinese he 河 ‘river’ can be used as proper names (for the Yellow River), as well as a common word for rivers in North China. Based on linguistic data, ethnological evidence and historical documents, this paper argues against these leading hypotheses and proposes that he originated from the Old Yi language, entered Chinese through language contact, and replaced shui which was from Old Qiang and later became the only common noun for river in North China.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122344303","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037663
Shichen Liang, Jianyu Zheng, Xuemei Tang, Renfen Hu, Zhiying Liu
In recent years, there has been a large number of publications that use distributed methods to track temporal changes in lexical semantics. However, most current researches only state the simple fact that the meaning of words has changed, lacking more detailed and in-depth analysis. We combine linguistic theory and word embedding model to study Chinese diachronic semantics. Specifically, two methods of word analogy and word similarity are associated with diachronic synonymy and diachronic polysemy respectively, and the aligned diachronic word embeddings are used to detect the changes of relationship between forms and meanings of words. Through experiments and case studies, our method achieves the ideal result. We also find that the evolution of Chinese vocabulary is closely related to social development, and there is a certain correlation between the polysemy and synonymy of the word meaning.
{"title":"Diachronic Synonymy and Polysemy: Exploring Dynamic Relation Between Forms and Meanings of Words Based on Word Embeddings","authors":"Shichen Liang, Jianyu Zheng, Xuemei Tang, Renfen Hu, Zhiying Liu","doi":"10.1109/IALP48816.2019.9037663","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037663","url":null,"abstract":"In recent years, there has been a large number of publications that use distributed methods to track temporal changes in lexical semantics. However, most current researches only state the simple fact that the meaning of words has changed, lacking more detailed and in-depth analysis. We combine linguistic theory and word embedding model to study Chinese diachronic semantics. Specifically, two methods of word analogy and word similarity are associated with diachronic synonymy and diachronic polysemy respectively, and the aligned diachronic word embeddings are used to detect the changes of relationship between forms and meanings of words. Through experiments and case studies, our method achieves the ideal result. We also find that the evolution of Chinese vocabulary is closely related to social development, and there is a certain correlation between the polysemy and synonymy of the word meaning.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126248466","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037649
Yuting Song, Biligsaikhan Batjargal, Akira Maeda
Recently, cross-lingual word embeddings have attracted a lot of attention, because they can capture semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform word embeddings space from one language to another. In this paper, we propose an advanced method for improving bilingual word embeddings by adding a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of Japanese language. On a benchmark data set of JapaneseEnglish bilingual lexicon induction, the proposed method achieved competitive performance compared to the method using a single mapping, with better results being found on original Japanese words.
{"title":"Improving Japanese-English Bilingual Mapping of Word Embeddings based on Language Specificity","authors":"Yuting Song, Biligsaikhan Batjargal, Akira Maeda","doi":"10.1109/IALP48816.2019.9037649","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037649","url":null,"abstract":"Recently, cross-lingual word embeddings have attracted a lot of attention, because they can capture semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform word embeddings space from one language to another. In this paper, we propose an advanced method for improving bilingual word embeddings by adding a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of Japanese language. On a benchmark data set of JapaneseEnglish bilingual lexicon induction, the proposed method achieved competitive performance compared to the method using a single mapping, with better results being found on original Japanese words.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133367257","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037650
T. Maruyama, Kazuhide Yamamoto
Recent text simplification approaches regard the task as a monolingual text-to-text generation inspired by machine translation. In particular, the transformer-based translation model outperform previous methods. Although machine translation approaches need a large-scale parallel corpus, parallel corpora for text simplification are very small compared to machine translation tasks. Therefore, we attempt a simple approach which fine-tunes the pre-trained language model for text simplification with a small parallel corpus. Specifically, we conduct experiments with the following two models: transformer-based encoder-decoder model and a language model that receives a joint input of original and simplified sentences, called TransformerLM. Thus, we show that TransformerLM, which is a simple text generation model, substantially outperforms a strong baseline. In addition, we show that fine-tuned TransformerLM with only 3,000 supervised examples can achieve performance comparable to a strong baseline trained by all supervised data.
{"title":"Extremely Low Resource Text simplification with Pre-trained Transformer Language Model","authors":"T. Maruyama, Kazuhide Yamamoto","doi":"10.1109/IALP48816.2019.9037650","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037650","url":null,"abstract":"Recent text simplification approaches regard the task as a monolingual text-to-text generation inspired by machine translation. In particular, the transformer-based translation model outperform previous methods. Although machine translation approaches need a large-scale parallel corpus, parallel corpora for text simplification are very small compared to machine translation tasks. Therefore, we attempt a simple approach which fine-tunes the pre-trained language model for text simplification with a small parallel corpus. Specifically, we conduct experiments with the following two models: transformer-based encoder-decoder model and a language model that receives a joint input of original and simplified sentences, called TransformerLM. Thus, we show that TransformerLM, which is a simple text generation model, substantially outperforms a strong baseline. In addition, we show that fine-tuned TransformerLM with only 3,000 supervised examples can achieve performance comparable to a strong baseline trained by all supervised data.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116837582","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037681
Lijie Wang, Mei Tu, Mengxia Zhai, Huadong Wang, Song Liu, Sang Ha Kim
Expression with honorifics is an important way of dressing up the language and showing politeness in Korean. For machine translation, generating honorifics is indispensable on the formal occasion when the target language is Korean. However, current Neural Machine Translation (NMT) models ignore generation of honorifics, which causes the limitation of the MT application on business occasion. In order to address the problem, this paper presents two strategies to improve Korean honorific generation ratio: 1) we introduce honorific fusion training (HFT) loss under the minimum risk training framework to guide the model to generate honorifics; 2) we introduce a data labeling (DL) method which tags the training corpus with distinctive labels without any modification to the model structure. Our experimental results show that the proposed two strategies can significantly improve the honorific generation ratio by 34.35% and 45.59%.
{"title":"Neural Machine Translation Strategies for Generating Honorific-style Korean","authors":"Lijie Wang, Mei Tu, Mengxia Zhai, Huadong Wang, Song Liu, Sang Ha Kim","doi":"10.1109/IALP48816.2019.9037681","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037681","url":null,"abstract":"Expression with honorifics is an important way of dressing up the language and showing politeness in Korean. For machine translation, generating honorifics is indispensable on the formal occasion when the target language is Korean. However, current Neural Machine Translation (NMT) models ignore generation of honorifics, which causes the limitation of the MT application on business occasion. In order to address the problem, this paper presents two strategies to improve Korean honorific generation ratio: 1) we introduce honorific fusion training (HFT) loss under the minimum risk training framework to guide the model to generate honorifics; 2) we introduce a data labeling (DL) method which tags the training corpus with distinctive labels without any modification to the model structure. Our experimental results show that the proposed two strategies can significantly improve the honorific generation ratio by 34.35% and 45.59%.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128095103","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}
Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037695
Yusha Zhang, Nankai Lin, Sheng-yi Jiang
English is the most widely used language in the world. With the spread and evolution of language, there are differences in the English text expression and reading difficulty in different regions. Due to the difference in the content and wording, English news in some countries is easier to understand than in others. Using an accurate and effective method to calculate the difficulty of text is not only beneficial for news writers to write easy-to-understand articles, but also for readers to choose articles that they can understand. In this paper, we study the differences in the text readability between most ASEAN countries, England and America. We compare the textual readability and syntactic complexity of English news texts among England, America and eight ASEAN countries (Indonesia, Malaysia, Philippines, Singapore, Brunei, Thailand, Vietnam, Cambodia). This paper selected the authoritative news media of each country as the research object. We used different indicators including Flesch-Kincaid Grade Level (FKG), Flesch Reading Ease Index (FRE), Gunning Fog Index (GF), Automated Readability Index (AR), Coleman-Liau Index (CL) and Linsear Write Index (LW) to measure the textual readability, and then applied L2SCA to analyze the syntactic complexity of news text. According to the analysis results, we used the hierarchical clustering method to classify the English texts of different countries into six different levels. Moreover, we elucidated the reasons for such readability differences in these countries.
英语是世界上使用最广泛的语言。随着语言的传播和演变，不同地区的英语文本表达和阅读难度存在差异。由于内容和措辞的不同，一些国家的英语新闻比另一些国家的英语新闻更容易理解。使用一种准确有效的方法来计算文本的难度，不仅有利于新闻作者写出易于理解的文章，也有利于读者选择自己能够理解的文章。本文研究了大多数东盟国家、英国和美国在文本可读性方面的差异。我们比较了英国、美国和东盟八个国家(印度尼西亚、马来西亚、菲律宾、新加坡、文莱、泰国、越南、柬埔寨)英语新闻语篇的可读性和句法复杂性。本文选取了各国的权威新闻媒体作为研究对象。本文采用Flesch- kincaid Grade Level (FKG)、Flesch Reading Ease Index (FRE)、Gunning Fog Index (GF)、Automated Readability Index (AR)、Coleman-Liau Index (CL)和Linsear Write Index (LW)等不同指标衡量新闻文本的可读性，并应用L2SCA对新闻文本的句法复杂度进行分析。根据分析结果，我们使用层次聚类方法将不同国家的英语文本划分为六个不同的层次。此外，我们还阐明了这些国家的可读性差异的原因。
{"title":"A Study on Syntactic Complexity and Text Readability of ASEAN English News","authors":"Yusha Zhang, Nankai Lin, Sheng-yi Jiang","doi":"10.1109/IALP48816.2019.9037695","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037695","url":null,"abstract":"English is the most widely used language in the world. With the spread and evolution of language, there are differences in the English text expression and reading difficulty in different regions. Due to the difference in the content and wording, English news in some countries is easier to understand than in others. Using an accurate and effective method to calculate the difficulty of text is not only beneficial for news writers to write easy-to-understand articles, but also for readers to choose articles that they can understand. In this paper, we study the differences in the text readability between most ASEAN countries, England and America. We compare the textual readability and syntactic complexity of English news texts among England, America and eight ASEAN countries (Indonesia, Malaysia, Philippines, Singapore, Brunei, Thailand, Vietnam, Cambodia). This paper selected the authoritative news media of each country as the research object. We used different indicators including Flesch-Kincaid Grade Level (FKG), Flesch Reading Ease Index (FRE), Gunning Fog Index (GF), Automated Readability Index (AR), Coleman-Liau Index (CL) and Linsear Write Index (LW) to measure the textual readability, and then applied L2SCA to analyze the syntactic complexity of news text. According to the analysis results, we used the hierarchical clustering method to classify the English texts of different countries into six different levels. Moreover, we elucidated the reasons for such readability differences in these countries.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125219339","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}
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