Data fusion has been shown to be a simple and effective way to improve retrieval results. Most existing data fusion methods combine ranked lists from different retrieval functions for a single given query—but in most real search settings, the diversity of retrieval functions required to achieve good fusion performance is not available. This paper presents a method for data fusion based on combining ranked lists from different queries that users could have entered for their information need, keeping the retrieval function fixed. We argue that if we can obtain a set of "possible queries" for an information need, we can achieve high effectiveness by fusing the rankings over the possible queries. In order to demonstrate effectiveness, we present experimental results on 5 different datasets covering tasks such as ad-hoc search, novelty and diversity search, and search in the presence of implicit user feedback. Our results show strong performances for our method, it is competitive with state-of-the-art methods on the same datasets, and in some cases outperforms them.
{"title":"Fusing Search Results from Possible Alternative Queries","authors":"Ashraf Bah Rabiou, Ben Carterette","doi":"10.1109/WI.2016.0105","DOIUrl":"https://doi.org/10.1109/WI.2016.0105","url":null,"abstract":"Data fusion has been shown to be a simple and effective way to improve retrieval results. Most existing data fusion methods combine ranked lists from different retrieval functions for a single given query—but in most real search settings, the diversity of retrieval functions required to achieve good fusion performance is not available. This paper presents a method for data fusion based on combining ranked lists from different queries that users could have entered for their information need, keeping the retrieval function fixed. We argue that if we can obtain a set of \"possible queries\" for an information need, we can achieve high effectiveness by fusing the rankings over the possible queries. In order to demonstrate effectiveness, we present experimental results on 5 different datasets covering tasks such as ad-hoc search, novelty and diversity search, and search in the presence of implicit user feedback. Our results show strong performances for our method, it is competitive with state-of-the-art methods on the same datasets, and in some cases outperforms them.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"29 1","pages":"606-609"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81210164","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}
Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we propose a simple model for transferring sentiment labels from words to tweets and vice versa by representing both tweets and words using feature vectors residing in the same feature space. Tweets are represented by standard NLP features such as unigrams and part-of-speech tags. Words are represented by averaging the vectors of the tweets in which they occur. We evaluate our approach in two transfer learning problems: 1) training a tweet-level polarity classifier from a polarity lexicon, and 2) inducing a polarity lexicon from a collection of polarity-annotated tweets. Our results show that the proposed approach can successfully classify words and tweets after transfer.
{"title":"From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach","authors":"Felipe Bravo-Marquez, E. Frank, B. Pfahringer","doi":"10.1109/WI.2016.29","DOIUrl":"https://doi.org/10.1109/WI.2016.29","url":null,"abstract":"Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we propose a simple model for transferring sentiment labels from words to tweets and vice versa by representing both tweets and words using feature vectors residing in the same feature space. Tweets are represented by standard NLP features such as unigrams and part-of-speech tags. Words are represented by averaging the vectors of the tweets in which they occur. We evaluate our approach in two transfer learning problems: 1) training a tweet-level polarity classifier from a polarity lexicon, and 2) inducing a polarity lexicon from a collection of polarity-annotated tweets. Our results show that the proposed approach can successfully classify words and tweets after transfer.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"52 1","pages":"145-152"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89923499","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 recent years, the amount of entities in large knowledge bases has been increasing rapidly. Such entities can help to bridge unstructured text with structured knowledge and thus be beneficial for many entity-centric applications. The key issue is to link entity mentions in text with entities in knowledge bases, where the main challenge lies in mention ambiguity. Many methods have been proposed to tackle this problem. However, most of the methods assume certain characteristics of the input mentions and documents, e.g., only named entities are considered. In this paper, we propose a context-aware approach to collective entity disambiguation of the input mentions in text with different characteristics in a consistent manner. We extensively evaluate the performance of our approach over 9 datasets and compare it with 14 state-of-the-art methods. Experimental results show that our approach outperforms the existing methods in most cases.
{"title":"Context-Aware Entity Disambiguation in Text Using Markov Chains","authors":"Lei Zhang, Achim Rettinger, Patrick Philipp","doi":"10.1109/WI.2016.0018","DOIUrl":"https://doi.org/10.1109/WI.2016.0018","url":null,"abstract":"In recent years, the amount of entities in large knowledge bases has been increasing rapidly. Such entities can help to bridge unstructured text with structured knowledge and thus be beneficial for many entity-centric applications. The key issue is to link entity mentions in text with entities in knowledge bases, where the main challenge lies in mention ambiguity. Many methods have been proposed to tackle this problem. However, most of the methods assume certain characteristics of the input mentions and documents, e.g., only named entities are considered. In this paper, we propose a context-aware approach to collective entity disambiguation of the input mentions in text with different characteristics in a consistent manner. We extensively evaluate the performance of our approach over 9 datasets and compare it with 14 state-of-the-art methods. Experimental results show that our approach outperforms the existing methods in most cases.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"19 1","pages":"49-56"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86103384","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}
If just consider one feature of sentences to calculate sentences similarity, the performance of system is difficult to reach a satisfactory level. This paper presents a method of combining the features of semantic and structural to compute sentences similarity. It first discusses the methods of calculating the semantic similarity of sentences through word embedding and Tongyici Cilin. Next, it discusses the methods of calculating the morphological similarity and order similarity of sentences, and then combines the features through the neutral network to calculate the total similarity of the sentences. We include results from an evaluation of the system's performance and show that a combination of the features works better than any single approach.
{"title":"A Research on Sentence Similarity for Question Answering System Based on Multi-feature Fusion","authors":"Haipeng Ruan, Yuan Li, Qinling Wang, Yu Liu","doi":"10.1109/WI.2016.0085","DOIUrl":"https://doi.org/10.1109/WI.2016.0085","url":null,"abstract":"If just consider one feature of sentences to calculate sentences similarity, the performance of system is difficult to reach a satisfactory level. This paper presents a method of combining the features of semantic and structural to compute sentences similarity. It first discusses the methods of calculating the semantic similarity of sentences through word embedding and Tongyici Cilin. Next, it discusses the methods of calculating the morphological similarity and order similarity of sentences, and then combines the features through the neutral network to calculate the total similarity of the sentences. We include results from an evaluation of the system's performance and show that a combination of the features works better than any single approach.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"64 1","pages":"507-510"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76301879","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 recent years, there has been increased interest in real-world event identification using data collected from social media, where theWeb enables the general public to post real-time reactions to terrestrial events - thereby acting as social sensors of terrestrial activity. Automatically extracting and categorizing activity from streamed data is a non-trivial task. To address this task, we present a novel event detection framework which comprises five main components: data collection, pre-processing, classification, online clustering and summarization. The integration between classification and clustering allows events to be detected - including "disruptive" events - incidents that threaten social safety and security, or could disrupt the social order. We evaluate our framework on a large-scale, real-world dataset from Twitter. We also compare our results to other leading approaches using Flickr MediaEval Event Detection Benchmark.
{"title":"Sensing Real-World Events Using Social Media Data and a Classification-Clustering Framework","authors":"Nasser Alsaedi, P. Burnap, O. Rana","doi":"10.1109/WI.2016.0039","DOIUrl":"https://doi.org/10.1109/WI.2016.0039","url":null,"abstract":"In recent years, there has been increased interest in real-world event identification using data collected from social media, where theWeb enables the general public to post real-time reactions to terrestrial events - thereby acting as social sensors of terrestrial activity. Automatically extracting and categorizing activity from streamed data is a non-trivial task. To address this task, we present a novel event detection framework which comprises five main components: data collection, pre-processing, classification, online clustering and summarization. The integration between classification and clustering allows events to be detected - including \"disruptive\" events - incidents that threaten social safety and security, or could disrupt the social order. We evaluate our framework on a large-scale, real-world dataset from Twitter. We also compare our results to other leading approaches using Flickr MediaEval Event Detection Benchmark.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"14 1","pages":"216-223"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79306731","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}
When mining user streams from social media, activity gaps are inevitable, which is known as the sparsity of user data. Such sparsity can significantly degrade the performance of a predictive system that relies on time-sensitive user content. To mitigate this issue, conventional approaches generally tend to discard periods with missing data. However, this solution leads to neglecting information generated by other users which, if utilized, could potentially enhance the quality of the predictive model. So the following question arises: is it possible to alleviate the impact of absent data while preserving the available content contributed within the same timespan? Despite the fact that this problem is well-known, it has not been thoroughly studied before. The goal of this work is to find a way of interpolating missing data from user's network and his previous activities. We investigate how different types of user profiles affect overall behavior predictability. Proposed models are evaluated on a case study of a micro-blogging system for the investment community.
{"title":"We Didn't Miss You: Interpolating Missing Opinions","authors":"Iuliia Chepurna, M. Makrehchi","doi":"10.1109/WI.2016.0094","DOIUrl":"https://doi.org/10.1109/WI.2016.0094","url":null,"abstract":"When mining user streams from social media, activity gaps are inevitable, which is known as the sparsity of user data. Such sparsity can significantly degrade the performance of a predictive system that relies on time-sensitive user content. To mitigate this issue, conventional approaches generally tend to discard periods with missing data. However, this solution leads to neglecting information generated by other users which, if utilized, could potentially enhance the quality of the predictive model. So the following question arises: is it possible to alleviate the impact of absent data while preserving the available content contributed within the same timespan? Despite the fact that this problem is well-known, it has not been thoroughly studied before. The goal of this work is to find a way of interpolating missing data from user's network and his previous activities. We investigate how different types of user profiles affect overall behavior predictability. Proposed models are evaluated on a case study of a micro-blogging system for the investment community.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"55 1","pages":"552-557"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86191852","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}
FAQs are the lists of common questions and answers on particular topics. Today one can find them in almost all web sites on the internet and they can be a great tool to give information to the users. Questions in FAQs are usually identified by the site administrators on the basis of the questions that are asked by their users. While such questions can respond to required information about a service, topic, or particular subject, they can not easily be distinguished from non-FAQ questions. This paper describes machine learning based parsing and question classification for FAQs. We demonstrate that questions for FAQs can be distinguished from other types of questions. Identification of specific features is the key to obtaining an accurate FAQ classifier. We propose a simple yet effective feature set including bag of words, lexical, syntactical, and semantic features. To evaluate our proposed methods, we gathered a large data set of FAQs in three different contexts, which were labeled by humans from real data. We showed that the SVM and Naive Bayes reach the accuracy of 80.3%, which is an outstanding result for the early stage research on FAQ classification. Experimental results show that the proposed approach can be a practical tool for question answering systems. To evaluate the accuracy of our classifier we have conducted an evaluation process and built the questionnaire. Therefore, we compared our classifier ranked questions with user rates and almost 81% similarity of the question ratings gives some confidence.
{"title":"Context Free Frequently Asked Questions Detection Using Machine Learning Techniques","authors":"Fatemeh Razzaghi, Hamed Minaee, A. Ghorbani","doi":"10.1109/WI.2016.0095","DOIUrl":"https://doi.org/10.1109/WI.2016.0095","url":null,"abstract":"FAQs are the lists of common questions and answers on particular topics. Today one can find them in almost all web sites on the internet and they can be a great tool to give information to the users. Questions in FAQs are usually identified by the site administrators on the basis of the questions that are asked by their users. While such questions can respond to required information about a service, topic, or particular subject, they can not easily be distinguished from non-FAQ questions. This paper describes machine learning based parsing and question classification for FAQs. We demonstrate that questions for FAQs can be distinguished from other types of questions. Identification of specific features is the key to obtaining an accurate FAQ classifier. We propose a simple yet effective feature set including bag of words, lexical, syntactical, and semantic features. To evaluate our proposed methods, we gathered a large data set of FAQs in three different contexts, which were labeled by humans from real data. We showed that the SVM and Naive Bayes reach the accuracy of 80.3%, which is an outstanding result for the early stage research on FAQ classification. Experimental results show that the proposed approach can be a practical tool for question answering systems. To evaluate the accuracy of our classifier we have conducted an evaluation process and built the questionnaire. Therefore, we compared our classifier ranked questions with user rates and almost 81% similarity of the question ratings gives some confidence.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"27 1","pages":"558-561"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89789465","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}
Existing approaches to coalition formation are generally gross simplifications of real problems of resource allocation where experience, reputation, and time optimization should be considered, although they are not usually studied together. To overcome this issue, this study proposes a dynamic and distributed social coalition formation model, that reproduces real-world environments where interactions are ruled by an underlying network that adapts itself based on the best updated reputation of local neighbors, in order to bring together individuals better suited for efficient cooperation. In this environment, agents possessing different levels of expertise must be organized to provide the most advantageous partnerships for the purpose of solving tasks, and an execution order of task's subtasks is defined to favor the use and release of agents' resources. To achieve this objective, we based our proposal on a coalitional skill game (CSG) approach, which organizes the use of resources by time commitment, and calculates and exploits the temporal reputation of heterogeneous agents to improve the utility of coalitions. Our experiments with different initial social networks allowed us to evaluate the effectiveness of this proposal and provided elements to exploit the advantages of an optimized social structure in a connected world.
{"title":"Dynamic Model for Social Coalition Formation Based on Expertise, Temporal Reputation and Time Commitment","authors":"C. Souza, F. Enembreck","doi":"10.1109/WI.2016.0052","DOIUrl":"https://doi.org/10.1109/WI.2016.0052","url":null,"abstract":"Existing approaches to coalition formation are generally gross simplifications of real problems of resource allocation where experience, reputation, and time optimization should be considered, although they are not usually studied together. To overcome this issue, this study proposes a dynamic and distributed social coalition formation model, that reproduces real-world environments where interactions are ruled by an underlying network that adapts itself based on the best updated reputation of local neighbors, in order to bring together individuals better suited for efficient cooperation. In this environment, agents possessing different levels of expertise must be organized to provide the most advantageous partnerships for the purpose of solving tasks, and an execution order of task's subtasks is defined to favor the use and release of agents' resources. To achieve this objective, we based our proposal on a coalitional skill game (CSG) approach, which organizes the use of resources by time commitment, and calculates and exploits the temporal reputation of heterogeneous agents to improve the utility of coalitions. Our experiments with different initial social networks allowed us to evaluate the effectiveness of this proposal and provided elements to exploit the advantages of an optimized social structure in a connected world.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"31 1","pages":"319-326"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89681968","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}
Online social networks play a vital role in spreading information in today's world. Interestingly, the spread of information is due to the existence of the underlying connectivity of the users. An important aspect in the propagation of information in a social network is the language of the connected users. Understanding the information propagation from the perspective of languages is of particular interest because we live in a world with a very diverse set of languages. This paper aims to explore the language networks that are formed as the basis of user interactions in Twitter (an online social network platform). Using Network Science approaches, we unveil the "Twitter language network" as a whole is a connected system of many different languages that acts as an enabler of information spread. The connected language structure arises due to the presence of many multilingual speakers. Our work also sheds light on the similarity of languages from a speaker-preference point of view.
{"title":"Exploring the World Languages in Twitter","authors":"P. Saha, R. Menezes","doi":"10.1109/WI.2016.0031","DOIUrl":"https://doi.org/10.1109/WI.2016.0031","url":null,"abstract":"Online social networks play a vital role in spreading information in today's world. Interestingly, the spread of information is due to the existence of the underlying connectivity of the users. An important aspect in the propagation of information in a social network is the language of the connected users. Understanding the information propagation from the perspective of languages is of particular interest because we live in a world with a very diverse set of languages. This paper aims to explore the language networks that are formed as the basis of user interactions in Twitter (an online social network platform). Using Network Science approaches, we unveil the \"Twitter language network\" as a whole is a connected system of many different languages that acts as an enabler of information spread. The connected language structure arises due to the presence of many multilingual speakers. Our work also sheds light on the similarity of languages from a speaker-preference point of view.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"25 1","pages":"153-160"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91078221","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}
Network functions virtualization is a new technology for the future internet that eliminates the dependency of the network function and the hardware requirement. The network functions virtualization provides a successful approach for meeting the increase in demand of the end-to-end (E2E) services with low operational and capital costs. Replacing the network specific purpose hardware (e.g. firewall) with a software implementation of the network functions in which a chain of Virtualized Network Functions (VNFs) can logically connect the end points and provide the desired network services. However, this approach is associated with the challenge of dynamically mapping the predefined VNFs onto the existing substrate network in an optimal way. In this paper, we propose a simple and effective approach for mapping the VNFs with the physical resources in a dynamic service request environment. The algorithm considers the priority dependency between the VNFs as a case of study, with the objective of minimizing the mapping blocking rate.
{"title":"Dynamic Allocation of Service Function Chains under Priority Dependency Constraint","authors":"M. Masoud, Sanghoon Lee, S. Belkasim","doi":"10.1109/WI.2016.0122","DOIUrl":"https://doi.org/10.1109/WI.2016.0122","url":null,"abstract":"Network functions virtualization is a new technology for the future internet that eliminates the dependency of the network function and the hardware requirement. The network functions virtualization provides a successful approach for meeting the increase in demand of the end-to-end (E2E) services with low operational and capital costs. Replacing the network specific purpose hardware (e.g. firewall) with a software implementation of the network functions in which a chain of Virtualized Network Functions (VNFs) can logically connect the end points and provide the desired network services. However, this approach is associated with the challenge of dynamically mapping the predefined VNFs onto the existing substrate network in an optimal way. In this paper, we propose a simple and effective approach for mapping the VNFs with the physical resources in a dynamic service request environment. The algorithm considers the priority dependency between the VNFs as a case of study, with the objective of minimizing the mapping blocking rate.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"6 1","pages":"684-688"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87637616","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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