Concept-based analysis of scientific literature

Tsai, Cary Chi‐Liang; Kundu, Gourab; Roth, Dan

doi:10.1145/2505515.2505613

Cited by 45 publications

(64 citation statements)

References 13 publications

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“…The bootstrapping based concept extraction approach proposed by Tsai et al [31] is currently the state-of-the-art technique for concept extraction in the scientific literature. Similar to our method, the bootstrapping algorithm uses a small number of pre-specified seeds of each target facet.…”

Section: Methodsmentioning

confidence: 99%

“…The final set of features are used to label the facet of concept mentions in the test set. Using a development set of 25 documents in both datasets, we set parameters ( k, n, t ) (the symbols have same defintions as in [31]) to (2000, 200, 2).…”

Section: Methodsmentioning

confidence: 99%

“…Table 3 depicts the effectiveness of our concept extraction method as compared to the noun-phrase chunking based concept extraction methods used by existing approaches [31]. Moreover, unlike the noun phrase extractor, our concept extractor treats two related words such as database systems and database management systems as the same concept.…”

Section: Facet Extractionmentioning

confidence: 99%

“…Several bootstrapping-based methods [31, 10, 30, 28, 20] have been proposed for identifying important concepts in medical domain and scientific corpus. [31] is currently the state-of-the-art, and also the baseline and starting point for our work.…”

Section: Related Workmentioning

confidence: 99%

“…, intra-document frequency, corpus-level popularity). Existing weakly-supervised methods for entity and concept recognition [11, 31, 10] are based on noun phrase chunking and supervised entity recognition that rely on only local syntactical features, and are trained over general-domain corpora ( e.g. , news articles).…”

Section: Introductionmentioning

confidence: 99%

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FacetGist

Siddiqui

Ren

Parameswaran

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Given the large volume of technical documents available, it is crucial to automatically organize and categorize these documents to be able to understand and extract value from them. Towards this end, we introduce a new research problem called Facet Extraction. Given a collection of technical documents, the goal of Facet Extraction is to automatically label each document with a set of concepts for the key facets (e.g., application, technique, evaluation metrics, and dataset) that people may be interested in. Facet Extraction has numerous applications, including document summarization, literature search, patent search and business intelligence. The major challenge in performing Facet Extraction arises from multiple sources: concept extraction, concept to facet matching, and facet disambiguation. To tackle these challenges, we develop FacetGist, a framework for facet extraction. Facet Extraction involves constructing a graph-based heterogeneous network to capture information available across multiple local sentence-level features, as well as global context features. We then formulate a joint optimization problem, and propose an efficient algorithm for graph-based label propagation to estimate the facet of each concept mention. Experimental results on technical corpora from two domains demonstrate that Facet Extraction can lead to an improvement of over 25% in both precision and recall over competing schemes.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Facet Extractionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FacetGist

Siddiqui

Ren

Parameswaran

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Predicting the impact of scientific concepts using full‐text features

McKeown

Daumé

Chaturvedi

et al. 2016

Asso for Info Science & Tech

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New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long‐term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties—including researchers and the general public—focus their attention within the vast scientific literature. In this paper we present a system that predicts the future impact of a scientific concept, represented as a technical term, based on the information available from recently published research articles. We analyze the usefulness of rich features derived from the full text of the articles through a variety of approaches, including rhetorical sentence analysis, information extraction, and time‐series analysis. The results from two large‐scale experiments with 3.8 million full‐text articles and 48 million metadata records support the conclusion that full‐text features are significantly more useful for prediction than metadata‐only features and that the most accurate predictions result from combining the metadata and full‐text features. Surprisingly, these results hold even when the metadata features are available for a much larger number of documents than are available for the full‐text features.

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Generating keyphrases for readers: A controllable keyphrase generation framework

Jiang

Meng

Huang

et al. 2023

Asso for Info Science & Tech

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With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users’ comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.

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Concept-based analysis of scientific literature

Cited by 45 publications

References 13 publications

FacetGist

FacetGist

Predicting the impact of scientific concepts using full‐text features

Generating keyphrases for readers: A controllable keyphrase generation framework

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