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On the adequacy of current empirical evaluations of formal models of categorization
Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)
Unsupervised Extraction of Representative Concepts from Scientific Literature
This paper studies the automated categorization and extraction of scientific
concepts from titles of scientific articles, in order to gain a deeper
understanding of their key contributions and facilitate the construction of a
generic academic knowledgebase. Towards this goal, we propose an unsupervised,
domain-independent, and scalable two-phase algorithm to type and extract key
concept mentions into aspects of interest (e.g., Techniques, Applications,
etc.). In the first phase of our algorithm we propose PhraseType, a
probabilistic generative model which exploits textual features and limited POS
tags to broadly segment text snippets into aspect-typed phrases. We extend this
model to simultaneously learn aspect-specific features and identify academic
domains in multi-domain corpora, since the two tasks mutually enhance each
other. In the second phase, we propose an approach based on adaptor grammars to
extract fine grained concept mentions from the aspect-typed phrases without the
need for any external resources or human effort, in a purely data-driven
manner. We apply our technique to study literature from diverse scientific
domains and show significant gains over state-of-the-art concept extraction
techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201
Bibliometric Survey on Incremental Learning in Text Classification Algorithms for False Information Detection
The false information or misinformation over the web has severe effects on people, business and society as a whole. Therefore, detection of misinformation has become a topic of research among many researchers. Detecting misinformation of textual articles is directly connected to text classification problem. With the massive and dynamic generation of unstructured textual documents over the web, incremental learning in text classification has gained more popularity. This survey explores recent advancements in incremental learning in text classification and review the research publications of the area from Scopus, Web of Science, Google Scholar, and IEEE databases and perform quantitative analysis by using methods such as publication statistics, collaboration degree, research network analysis, and citation analysis. The contribution of this study in incremental learning in text classification provides researchers insights on the latest status of the research through literature survey, and helps the researchers to know the various applications and the techniques used recently in the field
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201
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