What Sentence are you Referring to and Why? Identifying Cited Sentences in Scientific Literature

@inproceedings{AbuRaed2017WhatSA,
  title={What Sentence are you Referring to and Why? Identifying Cited Sentences in Scientific Literature},
  author={Ahmed Ghassan Tawfiq AbuRa'ed and Luis Chiruzzo and Horacio Saggion},
  booktitle={Recent Advances in Natural Language Processing},
  year={2017}
}
In the current context of scientific information overload, text mining tools are of paramount importance for researchers who have to read scientific papers and assess their value. Current citation networks, which link papers by citation relationships (reference and citing paper), are useful to quantitatively understand the value of a piece of scientific work, however they are limited in that they do not provide information about what specific part of the reference paper the citing paper is… 
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32 References

Purpose and Polarity of Citation: Towards NLP-based Bibliometrics

This paper analyzes the text that accompanies citations in scientific articles (which the authors term citation context) and proposes supervised methods for identifying citation text and analyzing it to determine the purpose and the polarity of citation.

Towards the Automatic Identification of the Nature of Citations

An algorithm is proposed, called CiTalO, to infer automatically the function of citations by means of Semantic Web technologies and NLP techniques, and some strengths and limitations of this approach are discussed.

Identifying Meaningful Citations

This work introduces 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, and proposes a supervised classification approach that addresses this task with a battery of features.

CiTO, the Citation Typing Ontology

This paper describes CiTO and illustrates its usefulness both for the annotation of bibliographic reference lists and for the visualization of citation networks, as well as harmonizing it with other ontologies describing bibliographies and the rhetorical structure of scientific discourse.

Argumentative zoning information extraction from scientific text

It is demonstrated that this type of text analysis can be used for generating usertailored and task-tailored summaries and for performing more informative citation analyses, and the application of a robust discourse and argumentation analysis can make text understanding techniques for practical document management more robust.

Natural Language Processing for Intelligent Access to Scientific Information

This tutorial provides an overview of the most relevant tasks related to the processing of scientific documents, including but not limited to the in-depth analysis of the structure of the scientific articles, their semantic interpretation, content extraction and summarization.

Sentiment Analysis of Citations Using Word2vec

Empirical research with the question: how well does word2vec work on the sentiment analysis of citations showed that word embeddings are effective on classifying positive and negative citations, however, hand-crafted features performed better for the overall classification.

Sentiment Analysis of Citations using Sentence Structure-Based Features

This paper uses a newly constructed annotated citation sentiment corpus to explore the effectiveness of existing and novel features, including n-grams, specialised science-specific lexical features, dependency relations, sentence splitting and negation features.

Dr. Inventor Framework: Extracting Structured Information from Scientific Publications

The first version of the Dr. Inventor Framework, a publicly available collection of scientific text mining components useful to prevent or at least mitigate this problem of textual analysis of scientific papers, is presented.

Overview of the CL-SciSumm 2016 Shared Task

This overview paper describes the participation and the official results of the second CL-SciSumm Shared Task, organized as a part of the Joint Workshop onBibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016), held in New Jersey,USA in June, 2016.