Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

Caragea, Cornelia; Bulgarov, Florin Adrian; Godea, Andreea; Gollapalli, Sujatha Das

doi:10.3115/v1/d14-1150

Cited by 97 publications

(87 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, most of these approaches consider only the textual content of a document or a document's local neighborhood, which is limited to textually-similar documents. In our recent work [3,1], we showed that, in addition to a document's textual content and textually-similar neighbors, other informative neighborhoods exist that have the potential to improve keyphrase extraction. For example, in a scholarly domain, research papers are not isolated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Supervised Keyphrase Extraction Models

Bulgarov

Caragea

2015

Proceedings of the 24th International Conference on World Wide Web

Self Cite

View full text Add to dashboard Cite

Keyphrases for a document provide a high-level topic description of the document. Given the number of documents growing exponentially on the Web in the past years, accurate methods for extracting keyphrases from such documents are greatly needed. In this study, we provide a comparison of existing supervised approaches to this task to determine the current best performing model. We use research articles on the Web as the case study.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Our citation context based approaches to keyphrase extraction [3,1] outperform many existing unsupervised (e.g., TextRank [8] and ExpandRank [9]) and supervised (e.g., Hulth's [5] and KEA [2]) approaches. However, a comparison with the approach by Medelyan et al [7], called Maui, was not performed.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Supervised Keyphrase Extraction Models

Bulgarov

Caragea

2015

Proceedings of the 24th International Conference on World Wide Web

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chuang et al (2012) proposed a model that incorporates a set of statistical and linguistic features (e.g., tf-idf, BM25, part-of-speech filters) for identifying descriptive terms in a text. Caragea et al (2014a) designed features based on information available in a document network (such as a citation network) and used them with traditional features in a supervised framework.…”

Section: Related Workmentioning

confidence: 99%

PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents

Florescu¹,

Caragea²

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

Self Cite

258

170

View full text Add to dashboard Cite

The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document's content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word's occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09%.

show abstract

“…keyphrases and key sentences, is relevant to our problem. There are two types of extraction, i.e., supervised [2], [6], [7], [9] and unsupervised methods [1], [3], [4], [8], [10], [11]. Natural language processing techniques [12], [13], [14] have also been used for keyphrase extraction.…”

Section: Related Workmentioning

confidence: 99%

Bidirectional Extraction of Phrases for Expanding Queries in Academic Paper Retrieval

Win¹,

Masada²

2016

ijarai

View full text Add to dashboard Cite

Abstract-This paper proposes a new method for query expansion based on bidirectional extraction of phrases as word n-grams from research paper titles. The proposed method aims to extract information relevant to users' needs and interests and thus to provide a useful system for technical paper retrieval. The outcome of proposed method are the trigrams as phrases that can be used for query expansion. First, word trigrams are extracted from research paper titles. Second, a co-occurrence graph of the extracted trigrams is constructed. To construct the co-occurrence graph, the direction of edges is considered in two ways: forward and reverse. In the forward and reverse co-occurrence graphs, the trigrams point to other trigrams appearing after and before them in a paper title, respectively. Third, Jaccard similarity is computed between trigrams as the weight of the graph edge. Fourth, the weighted version of PageRank is applied. Consequently, the following two types of phrases can be obtained as the trigrams associated with the higher PageRank scores. The trigrams of the one type, which are obtained from the forward co-occurrence graph, can form a more specific query when users add a technical word or words before them. Those of the other type, obtained from the reverse co-occurrence graph, can form a more specific query when users add a technical word or words after them. The extraction of phrases is evaluated as additional features in the paper title classification task using SVM. The experimental results show that the classification accuracy is improved than the accuracy achieved when the standard TF-IDF text features are only used. Moreover, the trigrams extracted by the proposed method can be utilized to expand query words in research paper retrieval.

show abstract

Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach

Cited by 97 publications

References 41 publications

A Comparison of Supervised Keyphrase Extraction Models

A Comparison of Supervised Keyphrase Extraction Models

PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents

Bidirectional Extraction of Phrases for Expanding Queries in Academic Paper Retrieval

Contact Info

Product

Resources

About