• Corpus ID: 2538517

Identifying Meaningful Citations

@inproceedings{Valenzuela2015IdentifyingMC,
  title={Identifying Meaningful Citations},
  author={Marco Valenzuela and Vu A. Ha and Oren Etzioni},
  booktitle={AAAI Workshop: Scholarly Big Data},
  year={2015}
}
We introduce the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort. [] Key Method We annotate a dataset of approximately 450 citations with this information, and release it publicly. We propose a supervised classification approach that addresses this task with a battery of features that range from citation counts to where the citation appears in the body of the paper, and show that,our approach…

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References

SHOWING 1-10 OF 25 REFERENCES

Measuring academic influence: Not all citations are equal

TLDR
The hip‐index, a model for predicting academic influence that achieves good performance on this data set using only four features, was found, among those evaluated, those based on the number of times a reference is mentioned in the body of a citing paper.

Unsupervised prediction of citation influences

TLDR
A probabilistic topic model is devised that explains the generation of documents and incorporates the aspects of topical innovation and topical inheritance via citations, and its ability to predict the strength of influence of citations against manually rated citations is evaluated.

Automatic classification of citation function

TLDR
This work shows that the annotation scheme for citation function is reliable, and presents a supervised machine learning framework to automatically classify citation function, using both shallow and linguistically-inspired features, finding a strong relationship between citation function and sentiment classification.

CiteSeer: an automatic citation indexing system

TLDR
CiteSeer has many advantages over traditional citation indexes, including the ability to create more up-to-date databases which are not limited to a preselected set of journals or restricted by journal publication delays, completely autonomous operation with a corresponding reduction in cost, and powerful interactive browsing of the literature using the context of citations.

Joint latent topic models for text and citations

TLDR
This work addresses the problem of joint modeling of text and citations in the topic modeling framework with two different models called the Pairwise-Link-LDA and the Link-PLSA-Lda models, which combine the LDA and PLSA models into a single graphical model.

Logical Structure Recovery in Scholarly Articles with Rich Document Features

TLDR
SectLabel is described, a module that further develops existing software to detect the logical structure of a document from existing PDF files, using the formalism of conditional random fields.

Latent Topic Models for Hypertext

TLDR
This paper presents a probabilistic generative model for hypertext document collections that explicitly models the generation of links and shows how to perform EM learning on this model efficiently.

TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents

TLDR
The TopicFlow model can be a powerful visualization tool to track the diffusion of topics across a citation network and is competitive with the state-of-theart Relational Topic Models in predicting the likelihood of unseen text on two different data sets.

Digital Libraries and Autonomous Citation Indexing

TLDR
Digital libraries incorporating ACI can help organize scientific literature and may significantly improve the efficiency of dissemination and feedback and speed the transition to scholarly electronic publishing.

The PageRank Citation Ranking : Bringing Order to the Web

TLDR
This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.