This posting simply lists some of the books I’ve read and Perl modules I’ve explored in regards to the field of text mining.
Through my explorations of term frequency/inverse document frequency (TFIDF) I became aware of a relatively new field of study called text mining. In many ways, text mining is similar to data mining only applied to unstructured texts instead of database rows and columns. Think plain text books such as items from Project Gutenberg or the Open Content Alliance. Text mining is a process including automatic classification, clustering (similar but distinct from classification), indexing and searching, entity extraction (names, places, organization, dates, etc.), statistically significant keyword and phrase extraction, parts of speech tagging, and summarization.
As a librarian, I found the whole thing extremely fascinating, consequently I read more.
Books
I have found the following four books helpful. They have enabled me to learn about the principles of text mining.
- Bilisoly, R. (2008). Practical text mining with Perl. Wiley series on methods and applications in data mining. Hoboken, N.J.: Wiley. – Of all the books listed here, this one includes the most Perl programming examples, and it is not as scholarly as the balance of the list. Much of the book surrounds the description of regular expressions against texts. Its strongest suit is the creation of terminal-based concordance scripts. Very nice. Lot’s of fun. The concordances return very interesting results. The book does describe clustering techniques too, but the on the overall topic of automatic metadata generation the book is not very strong.
- Konchady, M. (2006). Text mining application programming. Charles River Media programming series. Boston, Mass: Charles River Media. – This book is a readable survey of text mining covering parts of speech (POS) tagging, information extraction, search engines, clustering, classification, summarization, and question/answer processing. Many models for each aspect of text mining are described, compared, and contrasted. To put the author’s knowledge into practice, the book comes with a CD containing a Perl library for text mining, sample applications, and CGI scripts. This library is freely available on the Web.
- Nugues, P. M. (2006). An introduction to language processing with Perl and Prolog: An outline of theories, implementation, and application with special consideration of English, French, and German. Cognitive technologies. Berlin: Springer. – Of the four books listed here, this one is probably the most dense. I found its Perl scripts used to parse text more useful than the ones in Bilisoly, but this one included no concordance applications. I also found the description of n-grams to be very interesting — the extraction of multi-word phrases. I suspect the model they describe can be extended to n number of words. This book also discusses parts of speech (POS) processing but it is the only one that describes how to really parse language. Think semantics, lexicons, discourse, and dialog. After the first couple of chapters the Perl examples disappear and give way to exclusively Prologue examples.
- Weiss, S. M. (2005). Text mining: Predictive methods for analyzing unstructured information. New York: Springer. – The complexity of this book lies between Konchady and Nugues; it includes a greater number of mathematical models than Konchady, but it is easier to read than Nugues. Broad topics include textual documents as numeric vectors, using text for prediction, information retrieval, clustering & classification, and looking for information in documents. Each chapter includes a section called “Historical and Bibliographical Remarks” which has proved to be very interesting reading.
When it comes to the process of text mining I found each of these books useful in their own right. Each provided me with ways to reading texts, parsing texts, counting words, counting phrases, and through the application of statistical analysis create lists and readable summaries denoting the “aboutness” of given documents.
Perl modules
As a Perl hacker I am interested in writing scripts putting into practice some of the things I learn. Listed here are a number of modules that have gotten me further along in regard to text mining:
- Lingua::EN::Fathom – This library outputs interesting statistics regarding a given document: number of words and the number of times each occurs, number of sentences, complexity of words, number of paragraphs, etc. Of greatest interest are numbers (Fog, Flesch, and Flesch-Kincaid) denoting the readability of the text. Quick. Easy. Useful.
- Lingua::EN::Keywords – Given a text, this library outputs a list of what it thinks are the most significant individual words in a document, sans stop words. Not fancy.
- Lingua::EN::NamedEntity – Given a text, I believe this library comes pre-trained to extract names, places, and organizations from texts. It returns a Perl data structure listing the probabilities of a word or phrase being any particular entity. It may need to be re-trained to work for your corpus.
- Lingua::EN::Semtags::Engine – Given text this module will return words and phrases in a relevancy ranked order. Initially, I have had some problems using this module because it seems to take a long time to return. On the other hand, it looks promising since it returns both individual words as well as phrases.
- Lingua::EN::Summarize – Given a text this library returns sentences it thinks encapsulates the essence of the document. The result is readable — grammatically correct. The process it uses to accomplish its task is self-proclaimed as unscientific.
- Lingua::EN::Tagger – This library marks up a document in pseudo XML with tags denoting parts of speech in a given document. To do this work it also can extract words, noun phrases, and sentences from a text. Zippy. Probability-based. Developers are expected to parse the tagged output and do analysis against it, such as count the number of times particular parts of speech occur.
- Lingua::StopWords – Returns a simple list of stop words. Easy, but I can’t figure out how customizable it is. “One person’s stop word list is another person research topic.”
- Net::Dict – A network interface to DICT (dictionary) servers. While the DICT protocol is a bit long in the tooth, and not quite as cool as Web interfaces to things like Google or Wikipedia, this module does provide a handy way to look up definitions, a complimentary functionality to WordNet.
- Text::Aspell – A Perl interface to GNU Aspell which is great for spell-checking applications.
- TextMine – This is a set of modules written by Manu Konchady the author of Text Mining Application Programming. It includes submodules named Cluster, Entity, Index, Pos, Quanda (Q & A), Summary, Tokens, and WordNet. While this set of modules is the most comprehensive I’ve seen, and while they are probably the most theoretically based interfacing with things like WordNet to be thorough, my initial experience has been a bit frustrating since scripts written against the libraries do not turn very quickly. Maybe I’m feeding them documents that are too large and if so, then the libraries are not necessarily scalable.
- WordNet – There are a bevy of modules providing functionality against WordNet — a “lexical database of English… Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations.” Any truly thorough text mining application of English will take advantage of WordNet.
Text mining and librarianship
Given the volume of “born digital” material being created, it is not possible to apply traditional library methods against them. The hand-crafted, heavy human touch process is not scalable. Given the amounts of mass digitized text being generated from the Google Books Project and the Open Content Alliance, new opportunities for literary analysis make themselves evident. Again, the traditional library processes can not fill the bill in these regards.
Text mining techniques offer possible solutions to these problems. Count words. Count phrases. Compare these words, phrases, and counts to other texts. Determine their statistical significance. Assign them to documents in the form of subject headings, keywords, author names, and other added entries in our metadata formats. Given large numbers of books, articles, and other “wordy” documents, learn how to “save the time of the reader” by summarizing these documents and ranking them in some sort of order in addition to alphabetical or date. Compare and contrast full text works by learning what words and types of words are used in documents. Are the words religious in nature? Mathematic and scientific? Poetic? Such things will provide additional means for understanding and interpreting everything from scholarly journal articles to works of classic fiction and philosophy. These techniques are not intended to replace existing methods of understanding and organization, but rather to supplement and build upon them. This is an evolutionary process.`
If libraries and librarians desire to remain relevant in the evolving information environment, then they will need to do the good work they do differently. The problem to be solved now-a-days is less about access and more about use. Text mining is one way of making the content of libraries more useful.