2015 First International Conference on Arabic Computational Linguistics (ACLing)最新文献_第2页

Islamic Fatwa Request Routing via Hierarchical Multi-label Arabic Text Categorization 基于分层多标签阿拉伯文本分类的伊斯兰法特瓦请求路由

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.28

Reda A. Zayed, Mohamed Farouk Abdel Hady, H. Hefny

{"title":"Islamic Fatwa Request Routing via Hierarchical Multi-label Arabic Text Categorization","authors":"Reda A. Zayed, Mohamed Farouk Abdel Hady, H. Hefny","doi":"10.1109/ACLING.2015.28","DOIUrl":"https://doi.org/10.1109/ACLING.2015.28","url":null,"abstract":"Multi-label classification (MLC) is concerned withlearning from examples where each example is associatedwith a set of labels in opposite to traditional single-labelclassification where an example typically is assigned a single label. MLC problems appear in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. The religious domain has become an interesting and challenging area for machine learning and natural language processing. A \"fatwa\" in the Islamic religion represents the legal opinion or interpretation that a qualified scholar (mufti) can give on issues related to the Islamic law. It is similar to the issue of legal opinions from courts in common-law systems. In this paper, a hierarchical classification system is introduced to automatically route incoming fatwa requests to the most relevant mufti. Each fatwa is associated to multiple categories by mufti where the categories can be organized in a hierarchy. The results on fatwa requests routing have confirmed the effective and efficient predictive performance of hierarchical ensembles of multi-label classifiers trained using the HOMER method and its variations compared to binary relevance which simply trains a classifier for each label independently.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128081390","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: 3

A System for Extracting Sentiment from Large-Scale Arabic Social Data 从大规模阿拉伯社会数据中提取情感的系统

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.17

Hao Wang, Vijay R. Bommireddipalli, Ayman Hanafy, Mohamed Bahgat, Sara Noeman, O. Emam

引用次数: 12

Identifying the Topic-Specific Influential Users Using SLM 使用SLM识别特定主题的有影响力用户

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.24

M. Shalaby, Ahmed Rafea

引用次数: 3

Combined Classification for Extracting Named Entities from Arabic Texts 从阿拉伯文本中提取命名实体的组合分类

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.15

Fériel Ben Fraj Trabelsi, C. Ben Othmane Zribi, Wiem Kouki

引用次数: 0

Enrichment of the Arabic Treebank ATB with Syntactic Properties 阿拉伯语树库ATB语法特性的充实

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.9

R. Bahloul, K. Haddar, P. Blache

引用次数: 0

A Hybrid Approach for Sentiment Classification of Egyptian Dialect Tweets 埃及方言推文情感分类的混合方法

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.18

A. Shoukry, Ahmed Rafea

{"title":"A Hybrid Approach for Sentiment Classification of Egyptian Dialect Tweets","authors":"A. Shoukry, Ahmed Rafea","doi":"10.1109/ACLING.2015.18","DOIUrl":"https://doi.org/10.1109/ACLING.2015.18","url":null,"abstract":"Sentiment analysis has recently become one of the growing areas of research related to text mining and natural language processing. The main task of sentiment classification is to classify a sentence (i.e. tweet, review, blog, comment, news, etc.) as holding an overall positive, negative or neutral sentiment. Most of the current studies related to this topic focus mainly on English texts with very limited resources available for other languages like Arabic, especially for the Egyptian dialect. In this research work, we would like to improve the performance measures of Egyptian dialect sentence-level sentiment analysis by proposing a hybrid approach which combines both the machine learning approach using support vector machines and the semantic orientation approach. Two methodologies were proposed, one for each approach, which were then joined, creating the hybrid proposed approach. The results obtained show significant improvements in terms of the accuracy, precision, recall and F-measure, indicating that our proposed hybrid approach is effective in sentence-level sentiment classification. Also, the results are very promising which encourages continuing in this line of research.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143096","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: 37

A Proposed Approach for Arabic Language Segmentation 一种阿拉伯语分词方法

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.13

Adnan Souri, Mohammed Al Achhab, Badr Eddine El Mouhajir

引用次数: 3

Tunisian Arabic aeb Wordnet: Current State and Future Extensions 突尼斯阿拉伯语aeb Wordnet:当前状态和未来扩展

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.7

Nadia Karmani Ben Moussa, Hsan Soussou, A. Alimi

{"title":"Tunisian Arabic aeb Wordnet: Current State and Future Extensions","authors":"Nadia Karmani Ben Moussa, Hsan Soussou, A. Alimi","doi":"10.1109/ACLING.2015.7","DOIUrl":"https://doi.org/10.1109/ACLING.2015.7","url":null,"abstract":"Nowadays, Internet communication and especially informal Internet communication such as social networks, blogs, etc. is directing politic, economic, financial and social environments all over the world. Consequently, Internet monitoring is taking more and more scale particularly in Tunisia suffering from unsteadiness since the politic revolution in 2011. In a Tunisian context, Internet communication is characterized by the increasing use of aeb language (i.e. an Arabic dialect called Tunisian Arabic). Therefore, Tunisian Internet monitoring needs primarily aeb language processing tools, especially an aeb lexicon. However, few aeb lexicon were developed seen the lack of written resources. Some of these lexicons are created from Arabic lexicons. They cover aeb lexicon originally Arabic and ignore the large borrowed aeb lexicon. Others are build using the informal Web. In fact, they need a rigorous linguistic verification, correction and validation. In this case, we suggest building a standard, large and robust Wordnet taking in charge phonetic. Our Wordnet is created by the expand approach used for EuroWordnet building as in [12], based on the bilingual English-Tunisian Arabic dictionary Peace corps dictionary prepared by the linguists: R. Ben abdelkader, A. Ayed and A. Naouar [13], and the last version of Princeton Wordnet PWN 3.1. Moreover, it is modelized according to ISO-LMF by a switable Wordnet-LMF model for aeb language. In this paper, we present aeb wordnet building approach, describe its current state and propose extensions.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115053181","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: 6

A Named Entities Recognition System for Modern Standard Arabic using Rule-Based Approach 基于规则方法的现代标准阿拉伯语命名实体识别系统

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.14

Hala Elsayed, T. Elghazaly

{"title":"A Named Entities Recognition System for Modern Standard Arabic using Rule-Based Approach","authors":"Hala Elsayed, T. Elghazaly","doi":"10.1109/ACLING.2015.14","DOIUrl":"https://doi.org/10.1109/ACLING.2015.14","url":null,"abstract":"Named Entity Recognition (NER) is a task in Information Extraction (IE). The Named Entity Recognition has become very important for Natural Language Processing (NLP). In this paper, we designed a system which enhanced the named entities recognition for Arabic language where the system was developed for Arabic nouns and entities extractions. The nouns extraction system is based on Arabic morphological, the Arabic grammar rules a lot of them are not used before. The noun extraction in the system uses no gazetteers and the system is combined with entities extraction system depending on gazetteers. The system extracts noun according to morphological Arabic and classify them into proper nouns entities, title entities, currency entities, percentage entities, countries entities, cities entities, nationality entities, number entities, places entities, date entities and time entities. The system applied algorithms for generate nationality entities from countries entities, and the system applied Regular Expression (RE) for extract numbers in digit format. The system is not needed to normalization into the text before extraction process. The system tested text that is in the Modern Standard Arabic (MSA), the corpus is in open text. The system achieves results in an average recall of 85%.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116857028","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: 6

Lexicon Based and Multi-Criteria Decision Making (MCDM) Approach for Detecting Emotions from Arabic Microblog Text 基于词汇的多准则决策(MCDM)阿拉伯语微博文本情感检测方法

2015 First International Conference on Arabic Computational Linguistics (ACLing) Pub Date : 2015-04-17 DOI: 10.1109/ACLING.2015.21

Ahmad M. Abd Al-Aziz, M. Gheith, A. Eldin

{"title":"Lexicon Based and Multi-Criteria Decision Making (MCDM) Approach for Detecting Emotions from Arabic Microblog Text","authors":"Ahmad M. Abd Al-Aziz, M. Gheith, A. Eldin","doi":"10.1109/ACLING.2015.21","DOIUrl":"https://doi.org/10.1109/ACLING.2015.21","url":null,"abstract":"Emotions serve as a communicative function both within the brain and within the social group. Most of previous opinion mining studies applied on Arabic microblog text to identify positive, negative or neutral polarity. This paper studies the problem of detecting multiple emotion classes in Arabic microblog text (e.g. Twitter). Incoming Arabic microblog text is classified into one of fine grained emotional classes {happiness, sadness, fear, anger, disgust or none} if exists or mixed emotion if text contains multiple emotions e.g. {Happiness/Fear} or {Anger/Disgust}. We applied a combined approach of lexicon approach and Multi-Criteria Decision Making approach. We use a conditioned plot to classify and analyze the text by generating a two dimensional graphic analysis space, one dimension represents observations (tweets) and the other represents our variables (5 emotional scores). The experimental results show that our proposed approach by using the conditioned plot able to classify text into different fine grained emotions, and also able to classify Arabic text with mixed emotions.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131028983","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: 17