Citation content analysis (<scp>CCA</scp>): A framework for syntactic and semantic analysis of citation content

Zhang, Guo; Ding, Ying; Milojević, Staša

doi:10.1002/asi.22850

Cited by 127 publications

(109 citation statements)

References 76 publications

(116 reference statements)

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“…It is proposed that this type of analysis be applied in the study of bibliographic citations, as part of citation content analysis, to detect the intention and disposition of the citing author to the cited work, and to give additional information to complement the calculation of the estimated impact of a publication to enhance its bibliometric analysis (Jbara and Radev, 2012). This analysis includes syntactic and semantic language relationships through speech and natural language processing and the explicit and implicit linguistic choices in the text to infer citation function and feelings of the author regarding the cited work (Zhang et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Other limitation of this method is that a citation is interpreted as an author being influenced by the work of another, without specifying type of influence (Zhang et al, 2013) which can be misleading concerning the true impact of a citation (Young et al, 2008). To better understand the influence of a scientific work it is advisable to broaden the range of indicators to take into account factors like the author's disposition towards the reference, because, for instance, a criticized quoted work cannot have the same weight than other that is used as starting point of a research.…”

Section: Analyzing Argumentative Discourse Units In Online Interactionsmentioning

confidence: 99%

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Proceedings of the First Workshop on Argumentation Mining

Lawrence¹,

Reed²,

Allen³

et al. 2014

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ii Introduction Argumentation mining is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within a document, e.g. the premises, conclusion, and argumentation scheme of each argument, as well as argument-subargument and argumentcounterargument relationships between pairs of arguments. Proposed applications of argumentation mining include improving information retrieval and information extraction, as well as providing end-user visualization and summarization of arguments. Sources of interest include not only formal genres, but also a variety of informal genres such as microtext, spoken meeting transcripts, and product reviews. In instructional contexts where argumentation is a pedagogically important tool for conveying and assessing students' command of course material, the written and diagrammed arguments of students (and the mappings between them) are educational data that can be mined for purposes of assessment and instruction. This is especially important given the wide-spread adoption of computer-supported peer review, computerized essay grading, and large-scale online courses and MOOCs.Success in argumentation mining will require interdisciplinary approaches informed by natural language processing technology, theories of semantics, pragmatics and discourse, knowledge of discourse of domains such as law and science, artificial intelligence, argumentation theory, and computational models of argumentation. In addition, it will require creation and annotation of high-quality corpora of argumentation from different types of sources in different domains.The goal of this workshop is to provide the first research forum devoted to argumentation mining in all domains of discourse. Suggested topics include but are not limited to:• Automatic identification of argument elements (e.g., premises and conclusion; data, claim and warrant), argumentation schemes, relationships between arguments in a document, and relationships to discourse goals (e.g. stages of a "critical discussion") and/or rhetorical strategies;• Creation/evaluation of argument annotation schemes, relationship of argument annotation to linguistic and discourse structure annotation schemes, (semi)automatic argument annotation methods and tools, and creation/annotation of high-quality shared argumentation corpora;• Processing strategies integrating NLP methods and AI models developed for argumentation such as argumentation frameworks; and• Applications of argument/argumentation mining to, e.g., mining requirements and technical documents, analysis of arguments in dialogue (meetings, etc.), opinion analysis and mining consumer reviews, evaluation of students' written arguments and argument diagrams, and information access (retrieval, extraction, summarization, and visualization) in scientific and legal documents. AbstractAutomated argumentation mining requires an adequate type system or annotation scheme for classifying the patterns of argument that succeed or fail in a corpus of le...

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Section: Discussionmentioning

confidence: 99%

Section: Analyzing Argumentative Discourse Units In Online Interactionsmentioning

confidence: 99%

Proceedings of the First Workshop on Argumentation Mining

Lawrence¹,

Reed²,

Allen³

et al. 2014

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“…The method used for the data analysis of the third step was the citation content analysis [24], founded on the content analysis [25] and grounded theory [26]. This allowed a systematic, replicable compression of materials from the first and second step as codes and testing of hypotheses about both the quantity and quality of the scholarly influence of CLEF eHealth in 2012-2017.…”

Section: Methodsmentioning

confidence: 99%

“…Its results were supplemented by the known journal extensions of the CLEF eHealth overviews and previously enlisted papers that use the CLEF eHealth datasets [8]. WNs were technical reports written by participants describing their participation in the lab.Citation data for the resulting publication data was collected from Google Scholarone of the most comprehensive citation data sources in general and in particular for computer science, which is the main field of many CLEF eHealth scientists [9][10][11].The method used for the data analysis of the third step was the citation content analysis [24], founded on the content analysis [25] and grounded theory [26]. This allowed a systematic, replicable compression of materials from the first and second step as codes and testing of hypotheses about both the quantity and quality of the scholarly influence of CLEF eHealth in 2012-2017.…”

mentioning

confidence: 99%

Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes (Preprint)

Suominen¹,

Kelly²,

Goeuriot³

2018

Preprint

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Background: The eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to gather researchers working on health text analytics and to provide them with annual workshop, shared development challenges/tasks, benchmark datasets, and software for processing and evaluation. The overall purpose of this initiative is to ease and support patients, their next-ofkin, clinical staff, health scientists, and healthcare policy makers in accessing, understanding, using, and authoring health information in a multilingual setting. Objective: This original research paper reports on the outcomes of the first six installations of CLEF eHealth from 2012 to 2017. The focus is on measuring and analysing the scholarly influence by reviewing CLEF eHealth papers, together with relevant citation metrics. Methods: A review and bibliometric study of the CLEF eHealth proceedings, working notes, and author-declared paper extensions was conducted. Citation data for these publications were collected from Google Scholar. Citation content analysis was used for the publications and their citations. Results: The large number of registrations, submissions, and citations demonstrate the substantial community interest in the tasks and their resources. In total, 718 teams have registered their interest in the tasks, leading to 130 teams submitting to the 15 tasks. 184 papers using CLEF eHealth data generated 1,299 citations, yielding a total scholarly citation influence of almost 963,000 citations for the 741 co-authors and included authors from 33 countries across the world. The tasks' evaluation outcomes contribute to the knowledge of the difficulty of the research challenges the tasks address and the applicability of particular methods in solving these challenges, with typically statistically significant improvements in processing quality. Conclusions: These outcomes encourage continuing to develop these technologies to address patient needs. Consequently, data and tools have been opened for future research and development and the CLEF eHealth initiative continues to run new challenges. Keywords: Evaluation Studies as Topic; Health Records; Information Extraction; Information Storage and Retrieval; Information Visualization; Patient Education as Topic; Speech Recognition; Systematic Reviews; Test-set Generation; Text Classification IntroductionHealth information refers to all health-related content in all data formats, document types, information systems, publication media, and languages from all organisations, states, and countries. The privacy-sensitive, official part of health information consists of data recorded in healthcare services when describing a given patient's health or healthcare (Figure 1). The accessibility of this data is defined as limited (i.e., private or confidential information) and it is recorded either on paper in health records or electronically in Electronic Health (eHealth) records. Some common synonyms or related terms include eHealth charts, data, documents, information, ...

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“…Social science theories like homophily [23,24] and social influence [38] suggest that node attributes are highly correlated with network structure, i.e., the formation of one depends on and also influences the other. Examples include the strong association between user posts and following relationships in microblogging, and high correlation of paper topics and citations in academic networks [41]. In various applications, such as sentiment analysis [13] and trust prediction [33], it has been shown that jointly exploiting the two information sources can enhance the learning performance.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Attributed Network Embedding

Huang¹,

Li²,

Hu³

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

260

164

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Network embedding is to learn low-dimensional vector representations for nodes in a network. It has shown to be effective in a variety of tasks such as node classification and link prediction. While embedding algorithms on pure networks have been intensively studied, in many real-world applications, nodes are often accompanied with a rich set of attributes or features, aka attributed networks. It has been observed that network topological structure and node attributes are often strongly correlated with each other. Thus modeling and incorporating node attribute proximity into network embedding could be potentially helpful, though non-trivial, in learning better vector representations. Meanwhile, real-world networks often contain a large number of nodes and features, which put demands on the scalability of embedding algorithms. To bridge the gap, in this paper, we propose an accelerated attributed network embedding algorithm AANE, which enables the joint learning process to be done in a distributed manner by decomposing the complex modeling and optimization into many sub-problems. Experimental results on several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

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Citation content analysis (CCA): A framework for syntactic and semantic analysis of citation content

Cited by 127 publications

References 76 publications

Proceedings of the First Workshop on Argumentation Mining

Proceedings of the First Workshop on Argumentation Mining

Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes (Preprint)

Accelerated Attributed Network Embedding

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