CURRENT APPROACHES TO ACCOUNTING: TEXT MINING

Text Mining : Approaches and Applications

The field of text mining seeks to extract useful information from unstructured textual data through the identification and exploration of interesting patterns. The techniques employed usually do not involve deep linguistic analysis or parsing, but rely on simple “bag-of-words” text representations based on vector space. Several approaches to the identification of patterns are discussed, includi...

Approaches to Accounting Theories

Two Approaches to Handling Noisy Variation in Text Mining

Variation and noise in textual database entries can prevent text mining algorithms from discovering important regularities. We present two novel methods to cope with this problem: (1) an adaptive approach to “hardening” noisy databases by identifying duplicate records, and (2) mining “soft” association rules. For identifying approximately duplicate records, we present a domain-independent two-l...

Approaches to Text Mining Arguments from Legal Cases

This paper describes recent approaches using text-mining to automatically profile and extract arguments from legal cases. We outline some of the background context and motivations. We then turn to consider issues related to the construction and composition of a corpora of legal cases. We show how a Context-Free Grammar can be used to extract arguments, and how ontologies and Natural Language Pr...

A review of text mining approaches and their function in discovering and extracting a topic

Background and aim: Four text mining methods are examined and focused on understanding and identifying their properties and limitations in subject discovery. Methodology: The study is an analytical review of the literature of text mining and topic modeling.  Findings: LSA could be used to classify specific and unique topics in documents that address only a single topic. The other three text min...