About: Creating Classification Models from Textual Descriptions of Companies Using Crunchbase

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Creating Classification Models from Textual Descriptions of Companies Using Crunchbase
Creator	Carvalho, Joao Batista, Fernando Felgueiras, Marco
source	PMC
abstract	This paper compares different models for multilabel text classification, using information collected from Crunchbase, a large database that holds information about more than 600000 companies. Each company is labeled with one or more categories, from a subset of 46 possible categories, and the proposed models predict the categories based solely on the company textual description. A number of natural language processing strategies have been tested for feature extraction, including stemming, lemmatization, and part-of-speech tags. This is a highly unbalanced dataset, where the frequency of each category ranges from 0.7% to 28%. Our findings reveal that the description text of each company contain features that allow to predict its area of activity, expressed by its corresponding categories, with about 70% precision, and 42% recall. In a second set of experiments, a multiclass problem that attempts to find the most probable category, we obtained about 67% accuracy using SVM and Fuzzy Fingerprints. The resulting models may constitute an important asset for automatic classification of texts, not only consisting of company descriptions, but also other texts, such as web pages, text blogs, news pages, etc.
has issue date	2020-05-18(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50146-4_51
has license	no-cc
sha1sum (hex)	8e8c3f7dc66a0a39262879230821e8b592af4c6e
schema:url	https://doi.org/10.1007/978-3-030-50146-4_51
resource representing a document's title	Creating Classification Models from Textual Descriptions of Companies Using Crunchbase
has PubMed Central identifier	PMC7274320
schema:publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems
resource representing a document's body	covid:8e8c3f7dc66a0a39262879230821e8b592af4c6e#body_text
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