Data Mining II

62 Pages·2016·7.59 MB·English

2. Text Mining
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Text Mining
Goals
To learn key problems and techniques in the mining one of the most common types of
data
To learn how to represent text numerically
To learn how to make use of enormous amounts of unlabeled data
To learn how to find co-occurring keywords in documents
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2.1 Basics of Text Representation and Analysis
basedon:
CharuAggarwal,DataMining-TheTextbook,Springer2015,Chapter13
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What is text mining?
Definition
Text mining is the use of automated methods for exploiting the enormous amount of
knowledge available in the (biomedical) literature.
Motivation
Most knowledge is stored in terms of texts, both in industry and in academia.
This alone makes text mining an integral part of knowledge discovery!
Furthermore, to make text machine-readable, one has to solve several recognition
(mining) tasks on text.
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Why text mining?
Text data is growing in an unprecedented manner
Digital libraries
Web and Web-enabled applications (e.g. Social networks)
Newswire services
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Text mining terminology
Important definitions
A set of features of text is also referred to as a lexicon.
A document can be either viewed as a sequence or multidimensional record.
A collection of documents is referred to as a corpus.
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Text mining terminology
Number of special characteristics of text data
Very sparse
Diverse length
Nonnegative statistics
Side information is often available, e.g. Hyperlink, meta-data
Lots of unlabeled data
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What is text mining?
Common tasks
Information retrieval: Find documents that are relevant to a user, or to a query in a
collection of documents
Document ranking: rank all documents in the collection
Document selection: classify documents into relevant and irrelevant
Information filtering: Search newly created documents for information that is relevant to
a user
Document classification: Assign a document to a category that describes its content
Keyword co-occurrence: Find groups of keywords that co-occur in many documents
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Evaluation text mining
Precision and Recall
Let the set of documents that are relevant to a query be denoted as {Relevant} and the
set of retrieved documents as {Retrieved}.
The precision is the percentage of retrieved documents that are relevant to the query
∣{Relevant}∩{Retrieved}∣
precision= (1)
∣{Retrieved}∣
The recall is the percentage of relevant documents that were retrieved by the query:
∣{Relevant}∩{Retrieved}∣
recall= (2)
∣{Relevant}∣
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Text representation
Tokenization
Tokenization is the process of identifying keywords in a document.
Not all words in a text are relevant.
Text mining ignores stop words.
Stop words form the stop list.
Stop lists are context-dependent.
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