Opinion Mining is a raising research field of interest, with its different applications derived by market needs to analyze product reviews or to assess the public opinion, for political reasons, during presidential campaigns. In this paper, we address an approach for improving accuracy of Opinion Mining in Arabic. In order to conduct our study we need Arabic linguistic resources for opinion mining. Investigating the available resources we found that the OCA corpus is available and sufficient to prove our approach. Experimental results showed that applying different parameters of the machine learning classifiers on the OCA corpus leads to increasing the accuracy of the Arabic Opinion Mining.
{"title":"Increasing the Accuracy of Opinion Mining in Arabic","authors":"Sasi Atia, K. Shaalan","doi":"10.1109/ACLING.2015.22","DOIUrl":"https://doi.org/10.1109/ACLING.2015.22","url":null,"abstract":"Opinion Mining is a raising research field of interest, with its different applications derived by market needs to analyze product reviews or to assess the public opinion, for political reasons, during presidential campaigns. In this paper, we address an approach for improving accuracy of Opinion Mining in Arabic. In order to conduct our study we need Arabic linguistic resources for opinion mining. Investigating the available resources we found that the OCA corpus is available and sufficient to prove our approach. Experimental results showed that applying different parameters of the machine learning classifiers on the OCA corpus leads to increasing the accuracy of the Arabic Opinion Mining.","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":"114509920","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}
Recently Arabic dialects are receiving attention from the NLP research community due to their high usage in social media. One of the challenges of sentiment analysis of social media is the use of dialects. Since our ongoing research is on sentiment analysis of Saudi tweets, we conduct a pilot study to discover the percentage of Modern Standard Arabic (MSA) use by Saudi tweeters. The preliminary results show that 80% of the tweets used in the study are in MSA. Some phenomena found about the use of dialect in Saudi tweets are highlighted.
{"title":"Towards Analyzing Saudi Tweets","authors":"Nora Al-Twairesh, H. Al-Khalifa, A. Al-Salman","doi":"10.1109/ACLING.2015.23","DOIUrl":"https://doi.org/10.1109/ACLING.2015.23","url":null,"abstract":"Recently Arabic dialects are receiving attention from the NLP research community due to their high usage in social media. One of the challenges of sentiment analysis of social media is the use of dialects. Since our ongoing research is on sentiment analysis of Saudi tweets, we conduct a pilot study to discover the percentage of Modern Standard Arabic (MSA) use by Saudi tweeters. The preliminary results show that 80% of the tweets used in the study are in MSA. Some phenomena found about the use of dialect in Saudi tweets are highlighted.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"54 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":"124634168","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}
There is a huge gap between the written form of Arabic, Modern Standard Arabic (MSA), and the different spoken Arabic dialects due to the big number of dialects. In addition, most Arabic data-sets are formed for MSA content. Traditional ways of identifying dialects of texts are time and money consuming. In addition, due to the morphological complexity of Arabic, the gender of the speaker may change structure of an Arabic sentence. Thus, dialects hold rich information (such as the origin of the speaker and the gender of the addressee). A Game With A Purpose (GWAP) called "3ammeya" is implemented to identify the dialects of Arabic sentences along with their MSA translations. Moreover, through the game, the gender of the speaker addressee are classified. The collected data will help construct an expandable and cheap corpus for dialect identification and translation to MSA.
{"title":"Building a Corpus for Arabic Dialects Using Games with a Purpose","authors":"Maya Osman, Caroline Sabty, Nada Sharaf, Slim Abdennadher","doi":"10.1109/ACLING.2015.10","DOIUrl":"https://doi.org/10.1109/ACLING.2015.10","url":null,"abstract":"There is a huge gap between the written form of Arabic, Modern Standard Arabic (MSA), and the different spoken Arabic dialects due to the big number of dialects. In addition, most Arabic data-sets are formed for MSA content. Traditional ways of identifying dialects of texts are time and money consuming. In addition, due to the morphological complexity of Arabic, the gender of the speaker may change structure of an Arabic sentence. Thus, dialects hold rich information (such as the origin of the speaker and the gender of the addressee). A Game With A Purpose (GWAP) called \"3ammeya\" is implemented to identify the dialects of Arabic sentences along with their MSA translations. Moreover, through the game, the gender of the speaker addressee are classified. The collected data will help construct an expandable and cheap corpus for dialect identification and translation to MSA.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"32 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":"127874579","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 subjectivity and sentiment analysis (SSA), there are two main requirements are necessary to improve sentiment analysis effectively in any language and genres, first, high coverage sentiment lexicon - where entries are tagged with semantic orientation (positive, negative and neutral) - second, tagged corpora to train the sentiment classifier. Much of research has been conducted in this area during the last decade, but the need of building these resources is still ongoing, especially for morphologically-Rich language (MRL) such as Arabic. In this paper, we present an automatic expandable wide coverage polarity lexicon of Arabic sentiment words, this lexical resource explicitly devised for supporting Arabic sentiment classification and opinion mining applications. The lexicon is built using a seed of gold-standard Arabic sentiment words which are manually collected and annotated with semantic orientation (positive or negative), and automatically expanded with sentiment orientation detection of the new sentiment words by exploiting some lexical information such as part-of-speech (POS) tags and using synset aggregation techniques from free online Arabic lexicons, thesauruses. We report efforts to expand a manually-built our polarity lexicon using different types of data. Finally, we used various tagged data to evaluate the coverage and quality of our polarity lexicon, moreover, to evaluate the lexicon expansion and its effects on the sentiment analysis accuracy. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and Arabic microblogs (hotel reservation, product reviews, and TV program comments).
{"title":"Automatic Expandable Large-Scale Sentiment Lexicon of Modern Standard Arabic and Colloquial","authors":"Hossam S. Ibrahim, Sherif M. Abdou, M. Gheith","doi":"10.1109/ACLING.2015.20","DOIUrl":"https://doi.org/10.1109/ACLING.2015.20","url":null,"abstract":"In subjectivity and sentiment analysis (SSA), there are two main requirements are necessary to improve sentiment analysis effectively in any language and genres, first, high coverage sentiment lexicon - where entries are tagged with semantic orientation (positive, negative and neutral) - second, tagged corpora to train the sentiment classifier. Much of research has been conducted in this area during the last decade, but the need of building these resources is still ongoing, especially for morphologically-Rich language (MRL) such as Arabic. In this paper, we present an automatic expandable wide coverage polarity lexicon of Arabic sentiment words, this lexical resource explicitly devised for supporting Arabic sentiment classification and opinion mining applications. The lexicon is built using a seed of gold-standard Arabic sentiment words which are manually collected and annotated with semantic orientation (positive or negative), and automatically expanded with sentiment orientation detection of the new sentiment words by exploiting some lexical information such as part-of-speech (POS) tags and using synset aggregation techniques from free online Arabic lexicons, thesauruses. We report efforts to expand a manually-built our polarity lexicon using different types of data. Finally, we used various tagged data to evaluate the coverage and quality of our polarity lexicon, moreover, to evaluate the lexicon expansion and its effects on the sentiment analysis accuracy. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and Arabic microblogs (hotel reservation, product reviews, and TV program comments).","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"6 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":"124902886","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}
Arabic Natural Language Processing (ANLP) has known an important development during the last decade. Nowadays, Several ANLP tools are developed such as morphological analyzers, syntactic parsers, etc. These tools are characterized by their diversity in terms of development languages used, inputs/outputs manipulated, internal and external representations of results, etc. This is mainly due to the lack of models and standards that govern their implementations. This diversity does not favor interoperability between these tools or their reuse in new advanced projects. In this article, we propose APIs and models for three types of tools namely: stemmers, morphological analyzers and syntactic parsers, using SAFAR platform. Our proposal is a step for standardizing all aspects shared by tools of the same type. We review also the issue of interoperability between these tools. Finally, we discuss pipeline processes.
{"title":"Arabic Natural Language Processing from Software Engineering to Complex Pipeline","authors":"Younes Jaafar, Karim Bouzoubaa","doi":"10.1109/ACLING.2015.11","DOIUrl":"https://doi.org/10.1109/ACLING.2015.11","url":null,"abstract":"Arabic Natural Language Processing (ANLP) has known an important development during the last decade. Nowadays, Several ANLP tools are developed such as morphological analyzers, syntactic parsers, etc. These tools are characterized by their diversity in terms of development languages used, inputs/outputs manipulated, internal and external representations of results, etc. This is mainly due to the lack of models and standards that govern their implementations. This diversity does not favor interoperability between these tools or their reuse in new advanced projects. In this article, we propose APIs and models for three types of tools namely: stemmers, morphological analyzers and syntactic parsers, using SAFAR platform. Our proposal is a step for standardizing all aspects shared by tools of the same type. We review also the issue of interoperability between these tools. Finally, we discuss pipeline processes.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"8 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":"127669776","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}
Question Answering Systems have emerged as a good alternative to search engines where they produce the desired information in a very precise way in the real time. However, one serious concern with the Question Answering system is that despite having answers of the questions in the knowledge base, they are not able to retrieve the answer due to mismatch between the words used by users and content creators. There has been a lot of research in the field of English and some European language Question Answering Systems to handle this issue. However, Arabic Question Answering Systems could not match the pace due to some inherent difficulties with the language itself as well as due to lack of tools available to assist the researchers. In this paper, we are presenting a method to add semantically equivalent keywords in the questions by using semantic resources. The experiments suggest that the proposed research can deliver highly accurate answers for Arabic questions.
{"title":"Semantic Based Query Expansion for Arabic Question Answering Systems","authors":"Hani Al-Chalabi, S. Ray, K. Shaalan","doi":"10.1109/ACLING.2015.25","DOIUrl":"https://doi.org/10.1109/ACLING.2015.25","url":null,"abstract":"Question Answering Systems have emerged as a good alternative to search engines where they produce the desired information in a very precise way in the real time. However, one serious concern with the Question Answering system is that despite having answers of the questions in the knowledge base, they are not able to retrieve the answer due to mismatch between the words used by users and content creators. There has been a lot of research in the field of English and some European language Question Answering Systems to handle this issue. However, Arabic Question Answering Systems could not match the pace due to some inherent difficulties with the language itself as well as due to lack of tools available to assist the researchers. In this paper, we are presenting a method to add semantically equivalent keywords in the questions by using semantic resources. The experiments suggest that the proposed research can deliver highly accurate answers for Arabic questions.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"8 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":"132754531","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}
Lexical analysis can be a way to remove ambiguities in Arabic language. So their resolution is an important task in several Natural Language Processing (NLP) applications. In this context that this paper is inscribed. Our proposed resolution method is based essentially on the use of transducers on text automata. Indeed these transducers specify the lexical and contextual rules for Arabic language. They allow the resolution of lexical ambiguities. In order to achieve this resolution method, different types of lexical ambiguities are identified and studied to extract an appropriate set of rules. After that, we described lexical rules in ELAG [19] system (Elimination of Lexical Ambiguities by Grammars), which can delete paths representing morphosyntactic ambiguities. In addition, we present an experimentation implemented in the Unitex platform and conducted by various linguistic resources to obtain disambiguated syntactic structures suitable for the syntactic analysis. The obtained results are ambitious and can be improved by adding other rules and heuristics.
词法分析是消除阿拉伯语歧义的一种方法。因此，它们的解析是自然语言处理(NLP)应用中的一个重要任务。这篇论文就是在这样的背景下题写的。我们提出的解决方法基本上是基于在文本自动机上使用换能器。实际上，这些换能器指定了阿拉伯语的词汇和上下文规则。它们允许解决词汇歧义。为了实现这种解决方法，对不同类型的词汇歧义进行识别和研究，以提取合适的规则集。之后，我们描述了ELAG[19]系统中的词法规则(Elimination of lexical ambigities by Grammars)，它可以删除表示形态句法歧义的路径。此外，我们提出了在Unitex平台上实施的实验，并利用各种语言资源进行了实验，以获得适合句法分析的消歧句法结构。所获得的结果是雄心勃勃的，可以通过添加其他规则和启发式来改进。
{"title":"Toward the Resolution of Arabic Lexical Ambiguities with Transduction on Text Automaton","authors":"Nadia Ghezaiel, K. Haddar","doi":"10.1109/ACLING.2015.12","DOIUrl":"https://doi.org/10.1109/ACLING.2015.12","url":null,"abstract":"Lexical analysis can be a way to remove ambiguities in Arabic language. So their resolution is an important task in several Natural Language Processing (NLP) applications. In this context that this paper is inscribed. Our proposed resolution method is based essentially on the use of transducers on text automata. Indeed these transducers specify the lexical and contextual rules for Arabic language. They allow the resolution of lexical ambiguities. In order to achieve this resolution method, different types of lexical ambiguities are identified and studied to extract an appropriate set of rules. After that, we described lexical rules in ELAG [19] system (Elimination of Lexical Ambiguities by Grammars), which can delete paths representing morphosyntactic ambiguities. In addition, we present an experimentation implemented in the Unitex platform and conducted by various linguistic resources to obtain disambiguated syntactic structures suitable for the syntactic analysis. The obtained results are ambitious and can be improved by adding other rules and heuristics.","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":"123930325","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}
The ability to effectively organize textual information is a big challenge in intelligent text processing. With the increase in the amount of textual data being generated, this task is becoming more and more essential. In this paper we present an unsupervised computer-aided tool for automatically building classification schemes and taxonomies for enhancing the process of automated text classification. The tool utilizes the Wikipedia knowledge base and its categorization system to achieve its goal. Validation of the tool was done using a subset of a large language dataset obtained from the Google moderator series (Egypt 2.0) idea bank. The output of the tool was evaluated by comparing the similarity between the results obtained automatically from the tool, and those manually annotated by three different human evaluators, verifying the effectiveness of the tool. The tool showed effectiveness with a precision of 88.6% and recall of 81.2%.
{"title":"Unsupervised Data Driven Taxonomy Learning","authors":"Mahmoud M. Hosny, S. El-Beltagy, M.E. Allam","doi":"10.1109/ACLING.2015.8","DOIUrl":"https://doi.org/10.1109/ACLING.2015.8","url":null,"abstract":"The ability to effectively organize textual information is a big challenge in intelligent text processing. With the increase in the amount of textual data being generated, this task is becoming more and more essential. In this paper we present an unsupervised computer-aided tool for automatically building classification schemes and taxonomies for enhancing the process of automated text classification. The tool utilizes the Wikipedia knowledge base and its categorization system to achieve its goal. Validation of the tool was done using a subset of a large language dataset obtained from the Google moderator series (Egypt 2.0) idea bank. The output of the tool was evaluated by comparing the similarity between the results obtained automatically from the tool, and those manually annotated by three different human evaluators, verifying the effectiveness of the tool. The tool showed effectiveness with a precision of 88.6% and recall of 81.2%.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"184 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":"131719814","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}
Talaat Khalil, Amal Halaby, Muhammad Hammad, S. El-Beltagy
Arabic Twitter Sentiment Analysis has been gaining a lot of attention lately with supervised approaches being exploited widely. However, to date, there has not been an experimental study that examines how different configurations of the Bag of Words model, text representation scheme, can affect various supervised machine learning methods. The goal of the presented work is to do exactly that. Specifically, this work examines which configurations work best for each of three machine learning approaches that have shown good results when applied on the task of sentiment analysis, namely: Support Vector Machines, Compliment Naïve Bayes, and Multinomial Naïve Bayes. Experimenting with different datasets has shown that each of these classifiers has a Bag of Words configuration in conjunction with which, it consistently performs best. It also showed that some features are dataset dependent.
{"title":"Which Configuration Works Best? An Experimental Study on Supervised Arabic Twitter Sentiment Analysis","authors":"Talaat Khalil, Amal Halaby, Muhammad Hammad, S. El-Beltagy","doi":"10.1109/ACLING.2015.19","DOIUrl":"https://doi.org/10.1109/ACLING.2015.19","url":null,"abstract":"Arabic Twitter Sentiment Analysis has been gaining a lot of attention lately with supervised approaches being exploited widely. However, to date, there has not been an experimental study that examines how different configurations of the Bag of Words model, text representation scheme, can affect various supervised machine learning methods. The goal of the presented work is to do exactly that. Specifically, this work examines which configurations work best for each of three machine learning approaches that have shown good results when applied on the task of sentiment analysis, namely: Support Vector Machines, Compliment Naïve Bayes, and Multinomial Naïve Bayes. Experimenting with different datasets has shown that each of these classifiers has a Bag of Words configuration in conjunction with which, it consistently performs best. It also showed that some features are dataset dependent.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"117 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":"114365654","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}
Fatma Ben Mesmia, Nathalie Friburger, K. Haddar, D. Maurel
Arabic named entities (ANE) are often sources of information. That is why they are used by several applications of natural language processing (NLP) mainly in information retrieval. In order to improve the relevance of the information obtained, links to free resources can be established. Indeed, the recognition of these entities requires the use of adequate formalisms. In this paper, we propose an approach based on transducer cascades which allows the recognition of ANE more precisely the dates. This categorycan be an integral part in the events and the names of places. The implementation of the developed transducers cascades elaborated by using the CasSys tool is available under the Unitex platform. The results are motivating.
{"title":"Transducers Cascades for an Automatic Recognition of Arabic Named Entities in Order to Establish Links to Free Resources","authors":"Fatma Ben Mesmia, Nathalie Friburger, K. Haddar, D. Maurel","doi":"10.1109/ACLING.2015.16","DOIUrl":"https://doi.org/10.1109/ACLING.2015.16","url":null,"abstract":"Arabic named entities (ANE) are often sources of information. That is why they are used by several applications of natural language processing (NLP) mainly in information retrieval. In order to improve the relevance of the information obtained, links to free resources can be established. Indeed, the recognition of these entities requires the use of adequate formalisms. In this paper, we propose an approach based on transducer cascades which allows the recognition of ANE more precisely the dates. This categorycan be an integral part in the events and the names of places. The implementation of the developed transducers cascades elaborated by using the CasSys tool is available under the Unitex platform. The results are motivating.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"30 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":"122008817","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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