Pub Date : 2019-11-01DOI: 10.1109/IALP48816.2019.9037686
Dejian Li, Man Lan, Yuanbin Wu
The Chinese implicit discourse relationship recognition is more challenging than English due to the lack of discourse connectives and high frequency in the text. So far, there is no systematical investigation into the neural components for Chinese implicit discourse relationship. To fill this gap, in this work we present a component-based neural framework to systematically study the Chinese implicit discourse relationship. Experimental results showed that our proposed neural Chinese implicit discourse parser achieves the SOTA performance in CoNLL-2016 corpus.
{"title":"A Systematic Investigation of Neural Models for Chinese Implicit Discourse Relationship Recognition","authors":"Dejian Li, Man Lan, Yuanbin Wu","doi":"10.1109/IALP48816.2019.9037686","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037686","url":null,"abstract":"The Chinese implicit discourse relationship recognition is more challenging than English due to the lack of discourse connectives and high frequency in the text. So far, there is no systematical investigation into the neural components for Chinese implicit discourse relationship. To fill this gap, in this work we present a component-based neural framework to systematically study the Chinese implicit discourse relationship. Experimental results showed that our proposed neural Chinese implicit discourse parser achieves the SOTA performance in CoNLL-2016 corpus.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"43 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":"127896435","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/IALP48816.2019.9037712
Yadi Li, Lingling Mu, Hao Li, Hongying Zan
This paper proposes an answer ranking method used in Knowledge Base Question Answering (KBQA) system. This method first extracts the features of predicate sequence similarity based on sememe vector, predicates’ edit distances, predicates’ word co-occurrences and classification. Then the above features are used as inputs of the ranking learning algorithm Ranking SVM to rank the candidate answers. In this paper, the experimental results on the data set of KBQA system evaluation task in the 2016 Natural Language Processing & Chinese Computing (NLPCC 2016) show that, the method of word similarity calculation based on sememe vector has better results than the method based on word2vec. Its accuracy, recall rate and average F1 value respectively are 73.88%, 82.29% and 75.88%. The above results show that the word representation with knowledge has import effect on natural language processing.
{"title":"Automatic answer ranking based on sememe vector in KBQA","authors":"Yadi Li, Lingling Mu, Hao Li, Hongying Zan","doi":"10.1109/IALP48816.2019.9037712","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037712","url":null,"abstract":"This paper proposes an answer ranking method used in Knowledge Base Question Answering (KBQA) system. This method first extracts the features of predicate sequence similarity based on sememe vector, predicates’ edit distances, predicates’ word co-occurrences and classification. Then the above features are used as inputs of the ranking learning algorithm Ranking SVM to rank the candidate answers. In this paper, the experimental results on the data set of KBQA system evaluation task in the 2016 Natural Language Processing & Chinese Computing (NLPCC 2016) show that, the method of word similarity calculation based on sememe vector has better results than the method based on word2vec. Its accuracy, recall rate and average F1 value respectively are 73.88%, 82.29% and 75.88%. The above results show that the word representation with knowledge has import effect on natural language processing.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"23 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":"123498382","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/IALP48816.2019.9037675
Yumeto Inaoka, Kazuhide Yamamoto
We construct a Japanese grammatical simplification corpus and established automatic simplification methods. We compare the conventional machine translation approach, our proposed method, and a hybrid method by automatic and manual evaluation. The results of the automatic evaluation show that the proposed method exhibits a lower score than the machine translation approach; however, the hybrid method garners the highest score. According to those results, the machine translation approach and proposed method present different sentences that can be simplified, while the hybrid version is effective in grammatical simplification.
{"title":"Japanese grammatical simplification with simplified corpus","authors":"Yumeto Inaoka, Kazuhide Yamamoto","doi":"10.1109/IALP48816.2019.9037675","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037675","url":null,"abstract":"We construct a Japanese grammatical simplification corpus and established automatic simplification methods. We compare the conventional machine translation approach, our proposed method, and a hybrid method by automatic and manual evaluation. The results of the automatic evaluation show that the proposed method exhibits a lower score than the machine translation approach; however, the hybrid method garners the highest score. According to those results, the machine translation approach and proposed method present different sentences that can be simplified, while the hybrid version is effective in grammatical simplification.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"27 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":"127064645","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/IALP48816.2019.9037728
Bochuan Song, Bo Chai, Qiang Zhang, Quanye Jia
Traditional Chinese word segmentation (CWS) methods are based on supervised machine learning such as Condtional Random Fields(CRFs), Maximum Entropy(ME), whose features are mostly manual features. These manual features are often derived from local contexts. Currently, most state-of-art methods for Chinese word segmentation are based on neural networks. However these neural networks rarely introduct the user dictionary. We propose a LSTMbased Chinese word segmentation which can take advantage of the user dictionary. The experiments show that our model performs better than a popular segment tool in electricity domain. It is noticed that it achieves a better performance when transfered to a new domain using the user dictionary.
{"title":"A Chinese word segment model for energy literature based on Neural Networks with Electricity User Dictionary","authors":"Bochuan Song, Bo Chai, Qiang Zhang, Quanye Jia","doi":"10.1109/IALP48816.2019.9037728","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037728","url":null,"abstract":"Traditional Chinese word segmentation (CWS) methods are based on supervised machine learning such as Condtional Random Fields(CRFs), Maximum Entropy(ME), whose features are mostly manual features. These manual features are often derived from local contexts. Currently, most state-of-art methods for Chinese word segmentation are based on neural networks. However these neural networks rarely introduct the user dictionary. We propose a LSTMbased Chinese word segmentation which can take advantage of the user dictionary. The experiments show that our model performs better than a popular segment tool in electricity domain. It is noticed that it achieves a better performance when transfered to a new domain using the user dictionary.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"105 23 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":"127456072","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/IALP48816.2019.9037730
Mengxiang Wang, Cuiyan Ma
Constitutive role is one of the 4 qualia roles, which expresses a kind of constitutive relationship between nouns. According to the original definition and description characteristics, this paper divides the constitutive roles into two categories: materials and components. At the same time, combined with the previous methods of extracting the role automatically, this paper optimizes the method of extracting the role automatically. Relying on auxiliary grammatical constructions, we extract noun-noun pairs from large-scale corpus to extract descriptive features of constitutive roles, and then classifies these descriptive knowledge by manual double-blind proofreading. Finally, the author discusses the application of Chinese constitutive roles in word-formational analysis, syntactic analysis and synonym discrimination.
{"title":"Classified Description and Application of Chinese Constitutive Role","authors":"Mengxiang Wang, Cuiyan Ma","doi":"10.1109/IALP48816.2019.9037730","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037730","url":null,"abstract":"Constitutive role is one of the 4 qualia roles, which expresses a kind of constitutive relationship between nouns. According to the original definition and description characteristics, this paper divides the constitutive roles into two categories: materials and components. At the same time, combined with the previous methods of extracting the role automatically, this paper optimizes the method of extracting the role automatically. Relying on auxiliary grammatical constructions, we extract noun-noun pairs from large-scale corpus to extract descriptive features of constitutive roles, and then classifies these descriptive knowledge by manual double-blind proofreading. Finally, the author discusses the application of Chinese constitutive roles in word-formational analysis, syntactic analysis and synonym discrimination.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"18 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":"126439107","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/IALP48816.2019.9037664
Huiping Wang, Lijiao Yang, Huimin Xiao
Chinese grade reading for children has a broad application prospect. In this paper, Chinese textbooks for grade 1 to 6 of primary schools published by People’s Education Press are taken as data sets, and the texts are divided into 12 difficulty levels successively. The effective lexical indexes to measure the readability of texts are discussed, and a regression model to effectively measure the lexical difficulty of Chinese texts is established. The study firstly collected 30 indexes at the text lexical level from the three dimensions of lexical richness, semantic transparency and contextual dependence, selected the 7 indexes with the highest relevance to the text difficulty through Person correlation coefficient, and finally constructed a Regression to predict the text difficulty based on Lasso Regression, ElasticNet, Ridge Regression and other algorithms. The regression results show that the model fits well, and the predicted value could explain 89.3% of the total variation of text difficulty, which proves that the quantitative index of vocabulary difficulty of Chinese text constructed in this paper is effective, and can be applied to Chinese grade reading and computer automatic grading of Chinese text difficulty.
{"title":"Construction of Quantitative Index System of Vocabulary Difficulty in Chinese Grade Reading","authors":"Huiping Wang, Lijiao Yang, Huimin Xiao","doi":"10.1109/IALP48816.2019.9037664","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037664","url":null,"abstract":"Chinese grade reading for children has a broad application prospect. In this paper, Chinese textbooks for grade 1 to 6 of primary schools published by People’s Education Press are taken as data sets, and the texts are divided into 12 difficulty levels successively. The effective lexical indexes to measure the readability of texts are discussed, and a regression model to effectively measure the lexical difficulty of Chinese texts is established. The study firstly collected 30 indexes at the text lexical level from the three dimensions of lexical richness, semantic transparency and contextual dependence, selected the 7 indexes with the highest relevance to the text difficulty through Person correlation coefficient, and finally constructed a Regression to predict the text difficulty based on Lasso Regression, ElasticNet, Ridge Regression and other algorithms. The regression results show that the model fits well, and the predicted value could explain 89.3% of the total variation of text difficulty, which proves that the quantitative index of vocabulary difficulty of Chinese text constructed in this paper is effective, and can be applied to Chinese grade reading and computer automatic grading of Chinese text difficulty.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"31 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":"127626679","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/IALP48816.2019.9037655
Ying Chen, Jiajing Zhang, Bingying Ye, Chenfang Zhou
This study was designed to explore the prosodic patterns of focus in two dialects of Mandarin. One is Changchun Mandarin and the other is Nanjing Mandarin. The current paper compares the acoustics of their prosodic realization of focus in a production experiment. Similar to standard Mandarin, which uses in-focus expansion and concomitantly post-focus compression (PFC) to code focus, results in the current study indicate that both Changchun and Nanjing speakers produced significant in-focus expansion of pitch, intensity and duration and PFC of pitch and intensity in their Mandarin dialects. Meanwhile, the results show no significant difference of prosodic changes between Changchun and Nanjing Mandarin productions. These results reveal that PFC not only exists in standard Mandarin but also in Mandarin dialects.
{"title":"Prosodic Realization of Focus in Changchun Mandarin and Nanjing Mandarin","authors":"Ying Chen, Jiajing Zhang, Bingying Ye, Chenfang Zhou","doi":"10.1109/IALP48816.2019.9037655","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037655","url":null,"abstract":"This study was designed to explore the prosodic patterns of focus in two dialects of Mandarin. One is Changchun Mandarin and the other is Nanjing Mandarin. The current paper compares the acoustics of their prosodic realization of focus in a production experiment. Similar to standard Mandarin, which uses in-focus expansion and concomitantly post-focus compression (PFC) to code focus, results in the current study indicate that both Changchun and Nanjing speakers produced significant in-focus expansion of pitch, intensity and duration and PFC of pitch and intensity in their Mandarin dialects. Meanwhile, the results show no significant difference of prosodic changes between Changchun and Nanjing Mandarin productions. These results reveal that PFC not only exists in standard Mandarin but also in Mandarin dialects.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"75 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":"127673259","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/IALP48816.2019.9037674
Haoyi Cheng, Peifeng Li, Qiaoming Zhu
Event coreference resolution is a challenging task. To address the issues of the influence on event-independent information in event mentions and the flexible and diverse sentence structure in Chinese language, this paper introduces a GANN (Gated Attention Neural Networks) model to document-level Chinese event coreference resolution. GANN introduces a gated attention mechanism to select eventrelated information from event mentions and then filter noisy information. Moreover, GANN not only uses a single Cosine distance to calculate the linear distance between two event mentions, but also introduces multi-mechanisms, i.e., Bilinear distance and Single Layer Network, to further calculate the linear and nonlinear distances. The experimental results on the ACE 2005 Chinese corpus illustrate that our model GANN outperforms the state-of-the-art baselines.
{"title":"Employing Gated Attention and Multi-similarities to Resolve Document-level Chinese Event Coreference","authors":"Haoyi Cheng, Peifeng Li, Qiaoming Zhu","doi":"10.1109/IALP48816.2019.9037674","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037674","url":null,"abstract":"Event coreference resolution is a challenging task. To address the issues of the influence on event-independent information in event mentions and the flexible and diverse sentence structure in Chinese language, this paper introduces a GANN (Gated Attention Neural Networks) model to document-level Chinese event coreference resolution. GANN introduces a gated attention mechanism to select eventrelated information from event mentions and then filter noisy information. Moreover, GANN not only uses a single Cosine distance to calculate the linear distance between two event mentions, but also introduces multi-mechanisms, i.e., Bilinear distance and Single Layer Network, to further calculate the linear and nonlinear distances. The experimental results on the ACE 2005 Chinese corpus illustrate that our model GANN outperforms the state-of-the-art baselines.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"80 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":"131571031","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}
In this paper, we proposed a neural network architecture based on Time-Delay Neural Network (TDNN)Bidirectional Gated Recurrent Unit (BiGRU) for small-footprint keyWord spotting. Our model consists of three parts: TDNN, BiGRU and Attention Mechanism. TDNN models the time information and BiGRU extracts the hidden layer features of the audio. The attention mechanism generates a vector of fixed length with hidden layer features. The system generates the final score through vector linear transformation and softmax function. We explored the step size and unit size of TDNN and two attention mechanisms. Our model has achieved a true positive rate of 99.63% at a 5% false positive rate.
{"title":"An End-to-End Model Based on TDNN-BiGRU for Keyword Spotting","authors":"Shuzhou Chai, Zhenye Yang, Changsheng Lv, Weiqiang Zhang","doi":"10.1109/IALP48816.2019.9037714","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037714","url":null,"abstract":"In this paper, we proposed a neural network architecture based on Time-Delay Neural Network (TDNN)Bidirectional Gated Recurrent Unit (BiGRU) for small-footprint keyWord spotting. Our model consists of three parts: TDNN, BiGRU and Attention Mechanism. TDNN models the time information and BiGRU extracts the hidden layer features of the audio. The attention mechanism generates a vector of fixed length with hidden layer features. The system generates the final score through vector linear transformation and softmax function. We explored the step size and unit size of TDNN and two attention mechanisms. Our model has achieved a true positive rate of 99.63% at a 5% false positive rate.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"521 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":"131869190","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/IALP48816.2019.9037694
Joseph Marvin Imperial, R. Roxas, Erica Mae Campos, Jemelee Oandasan, Reyniel Caraballo, Ferry Winsley Sabdani, Ani Rosa Almaroi
Reading is an essential part of children’s learning. Identifying the proper readability level of reading materials will ensure effective comprehension. We present our efforts to develop a baseline model for automatically identifying the readability of children’s and young adult’s books written in Filipino using machine learning algorithms. For this study, we processed 258 picture books published by Adarna House Inc. In contrast to old readability formulas relying on static attributes like number of words, sentences, syllables, etc., other textual features were explored. Count vectors, Term FrequencyInverse Document Frequency (TF-IDF), n-grams, and character-level n-grams were extracted to train models using three major machine learning algorithms–Multinomial Naïve-Bayes, Random Forest, and K-Nearest Neighbors. A combination of K-Nearest Neighbors and Random Forest via voting-based classification mechanism resulted with the best performing model with a high average training accuracy and validation accuracy of 0.822 and 0.74 respectively. Analysis of the top 10 most useful features for each algorithm show that they share common similarity in identifying readability levels–the use of Filipino stop words. Performance of other classifiers and features were also explored.
阅读是儿童学习的重要组成部分。确定阅读材料的适当可读性水平将确保有效理解。我们展示了我们的努力，开发一个基线模型，用于使用机器学习算法自动识别用菲律宾语编写的儿童和青少年书籍的可读性。在这项研究中，我们处理了由Adarna House Inc.出版的258本绘本。与以往依赖单词数、句子数、音节数等静态属性的可读性公式不同，我们探索了其他文本特征。提取计数向量、Term Frequency、inverse Document Frequency (TF-IDF)、n-gram和字符级n-gram，并使用三种主要的机器学习算法(multinomial Naïve-Bayes、Random Forest和K-Nearest Neighbors)训练模型。通过基于投票的分类机制，将k近邻与随机森林相结合，得到了最佳的模型，平均训练精度和验证精度分别达到0.822和0.74。对每个算法最有用的前10个特征的分析表明，它们在识别可读性水平上有共同的相似性——使用菲律宾语停顿词。对其他分类器和特征的性能也进行了探讨。
{"title":"Developing a machine learning-based grade level classifier for Filipino children’s literature","authors":"Joseph Marvin Imperial, R. Roxas, Erica Mae Campos, Jemelee Oandasan, Reyniel Caraballo, Ferry Winsley Sabdani, Ani Rosa Almaroi","doi":"10.1109/IALP48816.2019.9037694","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037694","url":null,"abstract":"Reading is an essential part of children’s learning. Identifying the proper readability level of reading materials will ensure effective comprehension. We present our efforts to develop a baseline model for automatically identifying the readability of children’s and young adult’s books written in Filipino using machine learning algorithms. For this study, we processed 258 picture books published by Adarna House Inc. In contrast to old readability formulas relying on static attributes like number of words, sentences, syllables, etc., other textual features were explored. Count vectors, Term FrequencyInverse Document Frequency (TF-IDF), n-grams, and character-level n-grams were extracted to train models using three major machine learning algorithms–Multinomial Naïve-Bayes, Random Forest, and K-Nearest Neighbors. A combination of K-Nearest Neighbors and Random Forest via voting-based classification mechanism resulted with the best performing model with a high average training accuracy and validation accuracy of 0.822 and 0.74 respectively. Analysis of the top 10 most useful features for each algorithm show that they share common similarity in identifying readability levels–the use of Filipino stop words. Performance of other classifiers and features were also explored.","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":"121464491","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：