No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
CS-LAS: A scientific literature retrieval and analysis system based on term function recognition (TFR)
Abstract
We consider the problem of extant scientific literature search and analysis systems, and suggest that term function recognition can be very advantageous for fine-grain retrieval and semantic analyzation of a huge amount of scientific literature in a specific domain. We first elaborate the definition of term function (TF) in the scientific literature context, and the status and development of term function recognition (TFR) globally. Then, the computer science domain is taken as an example to design and implement a system called CS-LAS, which can provide users with several useful function modules based on TFR, such as scientific information search and browsing, domain hot spot discovery, evolution trend detection, and scholarly recommendation based on correlation analysis of functional terms.
... Term function (TF) refers to the specific semantic role that a word, a term or a phrase plays in scientific texts (Xin, Qikai and Wei 2017), including "topic," "method," "technology," etc. For instance, in the paper entitled "Knowledge discovery through co-word analysis" (He 1999), the TF of the term "knowledge discovery" is a "topic"; whereas, for the term "co-word analysis," it is a "method." ...
... More recently, a comprehensive framework for term function in academic texts was presented by Xin, Qikai and Wei (2017). In his study, Cheng categorized term functions into "domain-independent term function" (including "topic" and "method" in three levels) and "domain-related term function" (different sub categories in different domains). ...
... In prior studies regarding term function recognition (TFR), words in scientific papers that have been recognized include "topic," "method," "problem," "solution," "goal," "technology," "focus," "domain," etc. (Heffernan and Teufel 2018;Xin, Qikai and Wei 2017;Tsai, Kundu and Roth 2013;Kondo et al. 2009;Gupta and Manning 2011;Huang and Wan 2013). Concerning the term function of each authorselected keyword in each article, we present an annotation scheme for author-selected keywords, based on empirical work in content analysis. ...
Author-selected keywords have been widely utilized for indexing, information retrieval, bibliometrics and knowledge organization in previous studies. However, few studies exist concerning how author-selected keywords function semantically in scientific manuscripts. In this paper, we investigated this problem from the perspective of term function (TF) by devising indicators of the diversity and symmetry of keyword term functions in papers, as well as the intensity of individual term functions in papers. The data obtained from the whole Journal of Informetrics (JOI) were manually processed by an annotation scheme of keyword term functions, including "research topic," "research method," "research object," "research area," "data" and "others," based on empirical work in content analysis. The results show, quantitatively, that the diversity of keyword term function decreases, and the irregularity increases with the number of author-selected keywords in a paper. Moreover, the distribution of the intensity of individual keyword term function indicated that no significant difference exists between the ranking of the five term functions with the increase of the number of author-selected keywords (i.e., "research topic" > "research method" > "research object" > "research area" > "data"). The findings indicate that precise keyword related research must take into account the distinct types of author-selected keywords.
... In this paper, the term function refers to the semantic role of a segment or a paragraph in the related studies section (Cheng, 2015). Term function has been proved useful for academic retrieval and recommendation (Li, Cheng, & Lu, 2017). For example, when people conduct research on citation context recognition (CCR), the related work may involve problem statement on CCR, the CCR methods, datasets used in CCR, CCR related tools, CCR evaluation method, and the applications of CCR. ...
... Journal of Data and Information Science (Li, Cheng, & Lu, 2017 Cheng (2015) However, the classification of the term function is still obscure and not uniform. Considering the specific research problem in this article, we took some categories from existing literature and proposed three new categories of term function, which we will introduce in the next section. ...
Purpose
Researchers frequently encounter the following problems when writing scientific articles: (1) Selecting appropriate citations to support the research idea is challenging. (2) The literature review is not conducted extensively, which leads to working on a research problem that others have well addressed. This study focuses on citation recommendation in the related studies section by applying the term function of a citation context, potentially improving the efficiency of writing a literature review.
Design/methodology/approach
We present nine term functions with three newly created and six identified from existing literature. Using these term functions as labels, we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy. BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation. Then the term function information is applied to enhance the performance.
Findings
The experiments show that the term function-based methods outperform the baseline methods regarding the recall, precision, and F1-score measurement, demonstrating that term functions are useful in identifying valuable citations.
Research limitations
The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section. More recent deep learning models should be performed to future validate the proposed approach.
Practical implications
The citation recommendation strategy can be helpful for valuable citation discovery, semantic scientific retrieval, and automatic literature review generation.
Originality/value
The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users, improving the transparency, persuasiveness, and effectiveness of recommender systems.
... In this paper, term function refers to the semantic role or function of a segment, or a paragraph in related studies section (Cheng, 2015), which had been argued to be promising in scientific literature retrieval and scholarly recommendation (Li, Cheng, & Lu, 2017). For example, when people conduct research on citation context recognition (CCR), the related work may involve problem statement on CCR, the CCR methods, datasets used in CCR, CCR related tools, CCR evaluation method, and the applications of CCR. ...
... classification of term function(Li, Cheng, & Lu, 2017) ...
In the era of big scholarly data, researchers frequently encounter the following problems when writing scientific articles: 1) it's challenging to select appropriate references to support the research idea, and 2) literature review is not conducted extensively, which leads to working on a research problem that has been well addressed by others. Citation recommendation assists researchers to decide which article should be cited in a timely manner, as well as perform comprehensive and high-quality review of scientific literature. Some work has been done on this valuable and challenging task, but few of them focused on applying the semantic information of the citation context. This paper proposes a new citation recommendation strategy based on term function - the functions or roles of citation context in related studies section. We present 9 term functions as identified from the literature and annotated 531 research papers in 3 areas to evaluate our approach. The experiment results demonstrate that term functions are effective to identifying valuable references. The proposed method recommends more accurate citations for a given topic when compared to several baseline methods. The citation recommendation strategy can be helpful to generate automatic summaries and literature reviews.
The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets, and metrics, are extracted and used as artificial intelligence (AI) markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the named entity recognition model is used to extract AI markers from large-scale AI literature. A multi-stage self-paced learning strategy (MSPL) is proposed to address the negative influence of hard and noisy samples on the model training. Secondly, original papers are traced for AI markers. Statistical and propagation analyses are performed based on the tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters is explored. The above-mentioned mining based on AI markers yields many significant findings. For example, the propagation rate of the datasets gradually increases. The methods proposed by China in recent years have an increasing influence on other countries.
ResearchGate has not been able to resolve any references for this publication.