the book Practical Text Mining in Perl by Roger Bilisoy () when illustrating text . homeranking.info,.html,.xml,.pdf,.doc are among the possible formats for text data. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data: Computer Science Books @ homeranking.info ISBN: , Text mining is a new and exciting area of computer science research that tries to solve the crisis of.
|Language:||English, Spanish, Indonesian|
|Genre:||Business & Career|
|ePub File Size:||23.62 MB|
|PDF File Size:||17.49 MB|
|Distribution:||Free* [*Regsitration Required]|
Cambridge Core - Knowledge Management, Databases and Data Mining - The Text Mining Handbook - by Ronen Feldman. PDF | On Jan 1, , Feldman and others published The text mining handbook: Advanced approaches in analyzing unstructured data. Book Reviews The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data Ronen Feldman and James Sanger (Bar-Ilan University and.
By relating features by way of lexicons and ontologies. Single-link clusters are subgraphs of the MST. The IE process makes the number of different relevant entities and relationships on which the text mining is performed unbounded — typically thousands or even millions. Certainly, very different preprocessing techniques are required to prepare raw unstructured data for text mining than those traditionally encountered in knowledge discovery operations aimed at preparing structured data sources for classic data mining operations. Preprocessing tasks and core mining operations are the two most critical areas for any text mining system and typically describe serial processes within a generalized view of text mining system architecture. In a sense.
Sakthipriya Selvaraj. It is a relatively new research area, which has recently raised much interest among the research and industry communities, mainly due to the continuously increasing amount of information available on the Web and elsewhere.
The book by Feldman and Sanger is a thorough introduction to text mining, cov- ering the general architecture of text mining systems, along with the main techniques used by such systems. It addresses both the theory and practice of text mining, and it illustrates the different techniques with real-world scenarios and practical applications.
The book is structured into twelve chapters, which gradually introduce the area of text mining and related topics, starting with an introduction to the task of text mining, and ending with examples of practical applications from three different domains. The second part of the chapter lays down the general architecture of a text mining system, which also serves as a rough guide for the rest of the book, as it describes the main components of a text mining system that are described in detail in subsequent chapters.
Chapter 2 is one of the longest chapters in the book, and also one of the most dense in terms of newly introduced concepts. The second part of the chapter overviews the role of background knowledge in text mining. The authors describe several ontologies and lexicons, and show with evidence from a real-world example the FACT system they developed in the late s how background knowledge can be effectively integrated into text mining systems.
Chapter 3 is meant as a short introduction to text preprocessing techniques, includ- ing tokenization, tagging, and parsing. The next three chapters, 6, 7, and 8, address the task of information extraction, which is a key element in any text mining system.
Algorithm 1 in Section II. Increasing the threshold is relatively easy. Rajman and Besancon b provides a slightly different but also useful algorithm for accomplishing the same task.
In these cases. For percentage-type thresholds. Hearst also provides some interesting general background for the topic. Beyond Agrawal et al. Although focused more on visualization.
Clifton and Cooley provides a useful treatment of market basket problems and describes how a document may be viewed as a market basket of named entities. Section II. General changes to the threshold value should also be generally supported. Rajman and Besancon b provides the background for Section II. Although Algorithm 2 in Section II.
Montes-y-Gomez et al. Maximal associations are most recently and comprehensively treated in Amir et al. The algorithm example for the discovery of associations found in Section II. Rajman and Besancon and Feldman and Hirsh both point out that the discovery of frequent sets is the most computationally intensive stage of association generation.
Feldman and Dagan The analysis of sequences and trends with respect to knowledge discovery in structured data has been treated in several papers Mannila.
Blake and Pratt also makes some general points on this topic. Feldman and Dagan offers an early but still useful discussion of some of the considerations in approaching the isolation of interesting patterns in textual data. Nahm and Mooney Srikant and Agrawal Raghavan Keogh and Smyth Related work on the discovery of time series analysis has also been discussed Agrawal and Srikant Lent et al.
The term deviation sources was coined in Montes-y-Gomez et al. The notion of border sets was introduced. The FUP incremental updating approach comes from Cheung et al. These two works are among the most important entry points for the literature of trend analysis in text mining.
The trend graph described in Section II. Mannila et al. Use of the correlation measure r in the detection of ephemeral associations also comes from this source. Montesy-Gomez et al. The discussion of deviation detection in Section II. Much of the discussion of border sets in this section is an application of the border set ideas of Mannila and Toivonen to collections of text documents. Much of Section II. Much of the terminology in this section derives from Montes-y-Gomez et al. From these works.
Much of what has been written about the use of domain knowledge also referred to as background knowledge in classic data mining concerns its use as a mechanism for constraining knowledge discovery search operations. Such elements include those devoted to preprocessing.
Background knowledge. Some see a grouping of facts and relationships as a vocabulary constructed in such a way as to be both understandable by humans and readable by machines. A domain ontology.
Domains can exist for very broad areas of interest e. Perhaps another way of describing this is to say that a domain ontology houses all the facts and relationships for the domain it supports.
The GO knowledge base serves as a controlled vocabulary that describes gene products in terms of their associated biological processes.
Several researchers have reported that the GO knowledge base has been used for background knowledge and other purposes. In this controlled vocabulary. WordNet also supports a lexicon of about Domain Ontology with Domain Hierarchy: A domain ontology is a tuple O: GO actually comprises several different structured knowledge bases of information related to various species.
Version 1. An example of this from the GO molecular function vocabulary is the function concept transmembrane receptor protein-tyrosine kinase and its relationship to other function concepts.
Users of WordNet can query both its ontology and its lexicon. One example of a real-world ontology for a broad area of interest can be found in WordNet. Figure II. Domain Lexicon: A lexicon for an ontology O is a tuple Lex: Werner syndrome helicase ATP-dependent helicase e. Based on RefC. A lexicon such as that available with WordNet can serve as the entry point to background knowledge. Schematic of the Gene Ontology structure. From GO Consortium Domain Ontology with Lexicon: An ontology with lexicon is a pair O.
Using a lexicon. For the typical situation — such as the WordNet example — of an ontology with a lexicon. RefC consisting of a set SC. RefC s: Although there are may be any number of arguments about how background knowledge can enrich the value of knowledge discovery operations on document collections.
Bloom's syndrome protein Figure II. A tangible example of this kind of category. These categories can then be compared against some relevant external knowledge source to extract interesting attributes for these categories and relations between categories.
An example of this might be that. Perhaps the simplest method to integrate background knowledge into a text mining system is by using it in the construction of meaningful query constraints. Another common use of background knowledge is in the creation of consistent hierarchical representations of concepts in the document collection.
The category company could also have a set of relations to other categories such as IsAPartnerOf. During 45 The resulting query expression with constraint parameter would allow the user to specify the LHS and RHS of his or her query more carefully and meaningfully. Access to an ontology that stores both lexical references and relations allows for various types of resolution options. This background knowledge thus enabled FACT to. Centering on the association discovery query. Perhaps most importantly.
In this. It represented a focused effort at enhancing association discovery by means of several constraint types supplied by a background knowledge source. Using this background knowledge to construct meaningful constraints. FACT also exploited these constraints in how it structured its search for possible results. FACT allowed a user. FACT was able to exploit some basic forms of background knowledge.
FACT was able to leverage several attributes relating to a country size. Running against a document collection of newswire articles.
Implementation The document collection for the FACT system was created from the Reuters text categorization test collection. Reprinted with permission of John Wiley and Sons. The Reuters personnel tagged each document 47 The system provided an easy-to-use interface for a user to compose and execute an association discovery query. The Reuters documents were preassembled and preindexed with categories by personnel from Reuters Ltd.
This collection obviated the need to build any system elements to preprocess the document data by using categorization algorithms. The system then ran the fully constructed query against a document collection whose documents were represented by keyword annotations that had been pregenerated by a series of text categorization algorithms.
Result sets could be returned in ways that also took advantage of the background knowledge—informed constraints. From Feldman and Hirsh System architecture of FACT. Arab League. For experimentation with the Reuters data. FACT implemented a version of the two-phase Apriori algorithm.
This browser performed several functions. Upon completion of a query. For its main association-discovery algorithm. FACT executed its query code and passed a result set back to a specialized presentation tool.
In most cases. One interesting — albeit still informal and crude — experiment performed on the system was to see if there was any performance difference based on a comparison of CPU time between query templates with and without constraints.
This is a problem that can creep into almost any text mining system that attempts to integrate background knowledge. Feldman and Hirsh a provides an early discussion of various uses of background knowledge within a text mining system. Sections II. Hill et al. A large body of literature exists on the subject of WordNet.
Some text mining systems offer both. It is important in any implementation of a query language interface for designers of text mining systems to consider carefully the usage situations for the interfaces they provide. KDTL supports all three main patter-discovery query types i.
Also notice that each query contains one algorithmic statement and several constraint statements. Then, all subsequent constraint statements are applied to this component.
When specifying set relations, the user can optionally specify background predicates to be applied to the given expressions. Algorithmic statements: Constraint statements: From Feldman, Kloesgen, and Zilberstein a. Reprinted with permission of Springer Science and Business Media. In order to query only those associations that correlate between a set of countries including Iran and a person, the KDTL query expression would take the following form: Upon querying the document collection, one can see that, when Iran and Nicaragua are in the document, then, if there is any person in the document, Reagan will be in that document too.
As another example, if one wanted to infer which people were highly correlated with West Germany Reuters collection was from a period before the reunion of Germany , a query that looked for correlation between groups of one to three people and West Germany would be formulated. This type of example can also be used to show how background knowledge can be leveraged to eliminate trivial associations. For instance, if a user is very familiar with German politics and not interested in getting these particular associations, he or she might like to see associations between people who are not German citizens and Germany.
The tabbed dialog boxes in Figure II. Several different types of set constraints are supported, including background and numerical size constraints. See also Feldman and Hirsh Interface showing KDTL query results. Effective text mining operations are predicated on sophisticated data preprocessing methodologies.
Certainly, very different preprocessing techniques are required to prepare raw unstructured data for text mining than those traditionally encountered in knowledge discovery operations aimed at preparing structured data sources for classic data mining operations.
A large variety of text mining preprocessing techniques exist. All in some way attempt to structure documents — and, by extension, document collections. Quite commonly, different preprocessing techniques are used in tandem to create structured document representations from raw textual data. As a result, some typical combinations of techniques have evolved in preparing unstructured data for text mining. Two clear ways of categorizing the totality of preparatory document structuring techniques are according to their task and according to the algorithms and formal frameworks that they use.
Task-oriented preprocessing approaches envision the process of creating a structured document representation in terms of tasks and subtasks and usually involve some sort of preparatory goal or problem that needs to be solved such as extracting titles and authors from a PDF document.
Other preprocessing approaches rely on techniques that derive from formal methods for analyzing complex phenomena that can be also applied to natural language texts. In the end, the most advanced and meaning-representing features are used for the text mining, whereas the rest are discarded. The nature of the input representation and the output features is the principal difference between the preprocessing techniques. There are natural language processing NLP techniques, which use and produce domain-independent linguistic features.
Often the same algorithm is used for different tasks, constituting several different techniques. One of the important problems, yet unsolved in general, is to combine the processes of different techniques as opposed simply to combining the results. For instance, frequently part-of-speech ambiguities can easily be resolved by looking at the syntactic roles of the words.
Moreover, such redesigning makes the algorithms strongly coupled, precluding any possibility of changing them later. The problem is separated into a set of smaller subtasks, each of which is solved separately.
The subtasks can be divided broadly into three classes — preparatory processing, general purpose NLP tasks, and problem-dependent tasks. The complete hierarchy of text mining subtasks is shown in Figure III. Preparatory processing converts the raw representation into a structure suitable for further linguistic processing. For example, the raw input may be a PDF document, a scanned page, or even recorded speech. The task of the preparatory processing is to convert the raw input into a stream of text, possibly labeling the internal text zones such.
The number of possible sources for documents is enormous, and the number of possible formats and raw representations is also huge. Very complex and powerful techniques are sometimes required to convert some of those formats into a convenient form. However, one generic task that is often critical in text mining preprocessing operations and not widely covered in the literature of knowledge discovery might be called perceptual grouping. The general purpose NLP tasks process text documents using the general knowledge about natural language.
The tasks may include tokenization, morphological analysis, POS tagging, and syntactic parsing — either shallow or deep. The output can rarely be relevant for the end user and is typically employed for further problem-dependent processing. The domain-related knowledge, however, can often enhance the performance of the general purpose NLP tasks and is often used at different levels of processing.
In text mining, categorization and information extraction are typically used. Various experiments in psycholinguistics clearly demonstrate that the different stages of analysis — phonetic, morphological, syntactical, semantical, and pragmatical — occur simultaneously and depend on each other. The tasks they are able to perform include tokenization and zoning. POS tags provide information about the semantic content of a word. The approach most frequently found in text mining systems involves breaking the text into sentences and words.
Among these features are types of capitalization. Constituency grammars describe the syntactical structure of sentences in terms of recursively built phrases — sequences of syntactically grouped elements. Most constituency grammars distinguish between noun phrases. Syntactical Parsing Syntactical parsing components perform a full syntactical analysis of sentences according to a certain grammar theory.
The NLP components built in this way are valued for their generality. Tokenization Prior to more sophisticated processing. It is common for the tokenizer also to extract token features. The basic division is between the constituency and dependency grammars. The main challenge in identifying sentence boundaries in the English language is distinguishing between a period that signals the end of a sentence and a period that is part of a previous token like Mr.
The most common set of tags contains seven different tags Article.
POS taggers at some stage of their processing perform morphological analysis of words. Documents can be broken up into chapters. This can occur at several different levels. Each phrase may consist of zero or smaller Some systems contain a much more elaborate set of tags. POS tags divide words into categories based on the role they play in the sentence in which they appear. The nature of the features sharply distinguishes between the two main techniques: Instead of providing a complete analysis a parse of a whole sentence.
The set of all possible concepts or keywords is usually manually prepared. This view of the tagging approach is depicted in Figure III.
The hierarchy relation between the keywords is also prepared manually. Dependency grammars. Standard algorithms are too expensive for use on very large corpora and are not robust enough. For the purposes of information extraction. The text mining techniques normally expect the documents to be represented as sets of features.
For analysts and other knowledge workers. Without IE techniques. IE can save valuable time by dramatically speeding up discovery-type work. Lager Shallow Parsing The following papers discuss how to perform shallow parsing of documents: IE is perhaps the most prominent technique currently used in text mining preprocessing operations. Keller Brill Bridging the gap between raw data and actionable information. Schutze Constituency Grammars Information on constituency grammers can be found in Reape Kupiec Munoz et al.
Lewin et al. Grishman Carroll and Charniak Lin Cardie Lombardo Rambow and Joshi The study of automated text categorization dates back to the early s Maron Described abstractly.
Web page categorization under hierarchical catalogues. Applied to the domain of document management. In the document management domain. We start with the description of several common applications of text categorization. Then the formal framework and the 64 The main drawback of the knowledge engineering approach is what might be called the knowledge acquisition bottleneck — the huge amount of highly skilled labor and expert knowledge required to create and maintain the knowledge-encoding rules.
This chapter is organized as follows. Nowadays automated TC is applied in a variety of contexts — from the classical automatic or semiautomatic interactive indexing of texts to personalized commercials delivery. The document sorting problem has several features that distinguish it from the related tasks. Automatic indexing can be a part of automated extraction of metadata. The metadata describe a document in a variety of aspects. Next we survey the most commonly used algorithms solving the TC problem and wrap up with the issues of experimental evaluation and a comparison between the different algorithms.
Extraction of this metadata can be viewed as a document indexing problem. These are only a small set of possible applications. The task of assigning keywords from a controlled vocabulary to text documents is called text indexing. If the keywords are viewed as categories. Another example is e-mail coming into an organization.
The main difference is the requirement that each document belong to exactly one category. In Boolean information retrieval IR systems. The other applications described in this section usually constrain the number of categories to which a document may belong. The hierarchical structure of the set of categories is also uncommon. The Web documents contain links. Hierarchical Web page categorization.
Whenever the number of documents in a category exceeds k. Because it is usually computationally unfeasible to fully retrain the system after each document. For most of the TC systems. Another feature of the problem is the hypertextual nature of the documents.
The approximating function M: The value of F d. Such catalogues are very useful for direct browsing and for restricting the query-based search to pages belonging to a particular topic. Fixed thresholding assigns exactly k top-ranking categories to each document.
Given a document. The binary case is the most important because it is the simplest. In single-label categorization. This value is called a categorization status value CSV. This is called a document-pivoted categorization. Such a system is said to be doing the hard categorization. This is called a category-pivoted categorization. Various possible policies exist for setting the threshold. In this case. Proportional thresholding sets the threshold in such a way that the same fraction of the test set 67 The level of performance currently achieved by fully automatic systems.
In multilabel categorization the categories overlap. Binary categorization is a special case of single-label categorization in which the number of categories is two. A feature is simply an entity without internal structure — a dimension in the feature space.
The simplest is the binary in which the feature weight is either one — if the corresponding word is present in the document — or zero otherwise. The validation set is some portion of the training set that is not used for creating the model.
Yang Most TC systems at least remove the stop words — the function words and in general the common words of the language that usually do not contribute to the semantics of the documents and have no real added value. N is the number of all documents. The methods of giving weights to the features may vary. More complex weighting schemes are possible that take into account the frequencies of the word in the document.
The number of different words in big document collections can be huge. Experiments suggest that the latter method is usually superior to the others in performance Lewis a. The preprocessing step that removes the irrelevant words is called feature selection.
The most common bag-of-words model simply uses all words in a document as the features. A document is represented as a vector in this space — a sequence of features and their weights.
Many systems. The dimension of the bag-of-words feature space for a big collection can reach hundreds of thousands. The details of this method are described in Chapter V. By transforming the set of features it may be possible to create document representations that do not suffer from the problems inherent in those properties of natural language. A more systematic approach is latent semantic indexing LSI. Probably the simplest such measure is the document frequency DocFreq w.
The probabilities are computed as ratios of frequencies in the training data. More sophisticated measures of feature relevance exist that take into account the relations between features and the categories. Experiments show that both measures and several other measures can reduce the dimensionality by a factor of without loss of categorization quality — or even with a small improvement Yang and Pedersen Slonim and Tishby Li and Jain With unsupervised clustering.
Experiments conducted by several groups of researchers showed a potential in this technique only when the background information about categories was used for clustering Baker and McCallum Several LSI representations. Term clustering addresses the problem of synonymy by grouping together words with a high degree of semantic relatedness. These word groups are then used as features instead of individual words. In effect. There is no contradiction.
For the TC problem. Hayes built by the Carnegie group for Reuters. As a rule of thumb. Hayes et al. Four main issues need to be considered when using machine learning techniques to develop an application based on text categorization.
In the ML terminology. Hayes and Weinstein To calculate P d c. P d The marginal probability P d need not ever be computed because it is constant for all categories. Assuming the categorization is binary. Different priors are possible. Bayesian logistic regression BLR is an old statistical approach that was only recently applied to the TC problem and is quickly gaining popularity owing to its apparently very high performance.
The disadvantage of the Gaussian prior in the TC problem is that. Owing to computational cost constraints. With this prior. The alternative Laplace prior does achieve sparseness: A DT categorizes a document by starting at the root of the tree and moving successively downward via the branches whose The choice of a feature at each step is made by some information-theoretic measure such as information gain or entropy.
DNF rules are often built in a bottom-up fashion.. The learner then applies a series of generalizations e. The document is then assigned to the category that labels the leaf node.
A prototypical example for the category c is a vector w1. In this method. Cohen and Singer For this method to work.
Cohen b. At the end of the process. One of the attractive features of Ripper is its ability to bias the performance toward higher precision or higher recall as determined by the setting of the loss ratio parameter. The matrix element mij represents the degree of association between the ith feature and the jth category. One of the prominent examples of this family of algorithms is RIPPER repeated incremental pruning to produce error reduction Cohen a.
It can be applied to TC. In order to use the algorithm. More complex networks contain one or more hidden layers between the input and output layers. To decide whether a document d belongs to the category c.
Wiener Its performance. It has only two layers — the input and the output nodes. The distance-weighted version of kNN is a variation that weighs the contribution of each neighbor by its similarity to the test document. The Rocchio method is very easy to implement. For classifying a document. Follow the links below to find similar items on the Digital Library.
Access to some items in this collection may be restricted.
What responsibilities do I have when using this review? Dates and time periods associated with this review. Mihalcea, Rada, You Are Here: Physical Description 4 p.
Who People and organizations associated with either the creation of this review or its content. About Browse this Partner. What Descriptive information to help identify this review.
Degree Information Department: Computer Science and Engineering. Subjects Keywords data mining natural language processing text mining. Source Computational Linguistics,