In this age of Information Economy, data is generated from every digital computing device, handheld phone, workstation, server, and so on. Organizations are storing, processing, and analyzing data more than at any time in history. Well, if you’re looking for gold in a mine of information, Data Mining can surely help. Data Mining is the process of identifying and extracting patterns in large data sets to help answer business questions and predict future trends and behavior. However, a bountiful amount of useful data comes from everyday business documents. And, this is where Document Data in Data Mining comes in.
Documents are usually overlooked sources of information. Documents already contain a vast amount of information collected from various sources across an organization. It can help companies understand their customers better, develop more relevant and long-lasting relationships, and improve the overall customer experience. Document Data in Data Mining can populate inserter files, create indexes for archived pages, or convert messages into alternative forms. On top of that, data mined from documents can help companies in making smarter Marketing decisions and following regulatory directives. This article will help you understand the importance of Document Data in Data Mining. Let’s get started.
Table of Contents
What is Document Mining?
In this age of Information Economy, data is generated from every digital computing device, handheld phone, workstation, server, and so on. It’s very easy to become lost in the blend of data from multiple sources. Documents contain the majority of unstructured information which is difficult to access. Texts are much more complicated than numbers, time series, etc. Imagine trying to make heads or tails of such data. This is where the transition from Data Mining of structured data to Document Mining of unstructured data is made.
Document Mining is the process of finding and extracting useful patterns in a corpus of unstructured and vague textual information. Document Data in Data Mining involves Software Algorithms, Machine Learning, and Statistical Methods for Information Extraction, Natural Language Processing, and Document Summarization. Document Data in Data Mining is aimed at bringing forth previously unknown and unexplored information locked away in a mass of text.
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The Need For Getting Data From Documents
Documents as a source of data may sound odd at first, but there are advantages to exploiting this readily available information. Acquiring corporate data from the far-flung Databases spread throughout the enterprise requires time, resources, the service of IT Professionals, and knowledge of Data Structure. On the other hand, pulling the required information right from the documents is quite straightforward and takes less time.
Document archives are static and they contain all the historical data from the past. Discovering and extracting data from old versions of multiple systems requires time and resources in terms of money spent on outside service providers and the attention of skilled IT Professionals from the company. But collecting information from document archives can be accomplished easily. Pulling and dealing with the raw data from the Document Storehouse takes only a few weeks to achieve.
Use Cases of Document Data in Data Mining
The uses of Document Data in Data Mining are nearly unlimited. Document Data in Data Mining can be used to drive transpromo messaging, re-sequence print files, or combine mail pieces as part of a householding strategy. Extracting data from documents brings automated reprints within the reach of service providers, in case of document damages. Document Mining gives print service providers a competitive edge in the market by offering them additional functionality and insights. Sometimes, extracted data from documents can be combined with additional information beyond the source to develop entirely new documents.
Here are a few examples of how Document Data Mining has made advances in Printing technology and Digital marketing.
- Product purchase transaction details can be used to generate a QR Code which in turn can be used to deploy instructional or FAQs videos for customers. This will help the company increase customer satisfaction and cut down on product returns.
- Past payment transaction details can be used to mail a remittance cover only to customers who pay online.
- Document Data in Data Mining can help a Retail Chain find a correlation between the sale of Beer and Dippers on Friday afternoons, and adjust and optimize its marketing and inventory accordingly to increase profits.
- Document Data in Data Mining is also used to drive selective Marketing messages based on the address information on transactional documents. It can also be used by organizations for selling ad space in bills to advertisers.
- Product purchase information retrieved from documents can be used to drive Email follow-ups concerning customer reviews, feedback, and exciting offers.
Information Extraction in Document Data Mining
Information Extraction is considered to be one of the important components of the Document Mining process. It involves scanning texts with the aim of extracting facts present in the text(s). In Information Extraction, a lexicon is used to identify facts and the relationship among the information. Machine-Readable Dictionaries (MRD) can prove to be a valuable resource for automatic lexical acquisition. Let’s have a look at various techniques of IE.
Fact Extraction is concerned with the identification of individual facts contained within a document. The domain-specific knowledge is considered crucial here as pattern recognition for particular facts can be encoded. Here are a few techniques employed in Fact Extraction.
Pattern Matching uses common regular expressions to form the lowest level of extraction. It basically constructs an effective bottom-up parsing of the text. Pattern Matching is often employed on a large scale while processing tokens with a syntactic value.
The lexical-based approach to Fact Extraction begins with segregating the text into tokens and then identifying sentences. Various dictionaries and domain-specific lexicons are used to identify the likely context of words and phrases. By this stage, tokens that are proper names are recognized.
Syntactic and Semantic Structure
The next layer of extraction assigns a syntactic component to words and/or phrases in every sentence. Identifying nouns or verbs can be done while reading the local text, i.e., the current sentence. It further provides insights into the context of word occurrences in a sentence. At this stage, a trained Information Extraction system can start looking for semantic patterns in a sentence.
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Fact integration is mostly concerned with the problem of coreference. Individual facts in a document come together to form a bigger picture giving a detailed context. The initial stages of Fact Integration deal with resolving anaphoric references. For instance, a reference to “he” needs to be resolved to identify the referenced person. Moving to the later stages of integration, the concept of event merging becomes important. For example, let’s consider the 2 sentences:
- John was president.
- He was succeeded by Paul.
The two individual facts must be merged together to get more context. After combining the facts, it becomes evident that Paul was the President of the company. Going a bit deeper, the company can be identified in which Paul is the President. Hard-coded production systems may be employed for such purposes.
As the name suggests, this stage of Information Extraction basically deals with the end results of Document Data in Data Mining. This is a trivial phase of IE but is often very critical keeping in mind the end use of the extracted information. It becomes very important to represent and convey the insights gained from the extracted information. Visualization tools may be employed for this purpose.
What is Document Summarization?
Quite similar to Information Extraction, Document Summarization deals with the extraction of a small number of sentences that summarizes the concept of the document from the source document. Document Summarization relatively deals with a smaller amount of content material in a document while still retaining mostly the same information. Document extracts consisting of roughly 20% of the source content can be as informative as the full text of a document.
Difference between Data Mining and Document Data Mining
Data Mining refers to the process of extracting and structuring large raw datasets and recognizing the various patterns in the data through analytical, mathematical, and computational algorithms. This helps to generate new information and unlock various valuable insights. This valuable information helps public and private enterprises store, monitor, and analyze data for different purposes. Data Mining has been in existence for some time now and is a fairly mature technology. However, Data Mining traditionally deals with structured information involving numbers, time series, etc.
To access unstructured information in the form of documents (texts, words, audio, video, etc), Document Mining is used. Document Mining, however, is still in the conception phase. Although Text Mining tools are making advances, Image, Audio, and Video Mining tools are not yet available. The future of Document Data in Data Mining predominantly lies with the availability and capability of the mining tools.
With every organization generating data like never before, it is essential to extract valuable insights out of the vague sets of data. With 80 to 95% of corporate information stored in papers and electronic documents, Document Mining comes in handy to retrieve useful information from them. Document Data Mining helps companies make better marketing decisions.
This article introduced you to Document Data in Data Mining and took you through various aspects of it. However, it’s easy to become lost in a blend of data from multiple sources. Imagine trying to make heads or tails of such data. This is where Hevo comes in.
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