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eClassifier:Tool for Taxonomies

Scott Spanglerspangles@almaden.ibm.com

IBM Almaden Research CenterSan Jose, CA

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Assertions on Taxonomy Generation

• Manual methods are too labor intensive, limit scope and scale, and are not maintainable

• Canned taxonomies are a niche solution• There are many “natural” or “right”

taxonomies, even on the same collection• Clustering, canned taxonomies and other

methods are good starting points, but not enough

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Salient Features of eClassifier• Clustering algorithm independent

– bias towards speed for interaction• Classification algorithm independent

– evaluate multiple algorithms for given taxonomy– pick best algorithm for each level in taxonomy

• Multiple methods to seed taxonomy:– import, clustering, query based

• Multiple methods for evaluating, editing and validating taxonomies

• Given a taxonomy, analysis/discovery against structured and unstructured information

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eClassifier Principles

• Apply multiple text mining algorithms to textual data sets in a practical manner.

• Provide consistently good results, the goal is not perfection.

• Utilize domain expertise by giving the user control over the mining process.

• Provide tools, metrics and reports to draw useful conclusions from the analysis.

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The Mining Process

• Create a dictionary of terms (words and phrases)• Prune dictionary (prune irrelevant terms)• Cluster documents based on this dictionary• Examine the resulting taxonomy, modifying based on

domain expertise• Create multiple taxonomies (divide and conquer)• Do deeper analysis by creating keyword

classifications, comparing taxonomies, inspecting dictionary co-occurrence, examining recent trends

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The Class Table

For viewing and understanding each level in a taxonomy

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Understanding Class Metrics• Class Naming Convention

• Shortest possible name that covers the examples• “,” => OR• “&” => AND• X_Y => X followed by Y• NONE => no useful text• Miscellaneous => No easy description

• Cohesion• A measure of similarity between documents in the same class (0-

different terms, 100-same terms)• Distinctness

• A measure of similarity between documents in different classes (0-very similar, 100-very unique)

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Dictionary Tool• Edit -> Dictionary Tool• Use this to edit the

features on which the taxonomy is based

• Delete irrelevant or ambiguous terms

• Generate and edit synonyms

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Dictionary Generation Files• StopWords

• words excluded from the dictionary• Synonyms

• different forms of the same semantic term• IncludeWords

• words that always appear in dictionary• Stock Phrases

• text to be ignored in creating dictionary• Synonyms and Stock Phrases can be automatically

generated and then edited

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Refinement of Classes• Subclass Classes

• Subdivide an existing class into multiple subclass at the next level in the taxonomy

• Merge Classes• Delete Classes• Rename Class• Undo

• Don’t be afraid to try things• Save

• .obj files contain all information eClassifier uses• .class files contain class membership

• Read

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Class View• For understanding the

concepts and contents of a given class

• View the text• Most typical• Least typical

• View the source Web page• View distinguishing terms• View deduced rules for

classification and related documents

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Keyword Searching• Edit->Keyword Search• Search for Dictionary terms• Use “and” , “or” and “_”• Searching within a class• Related Words• Look at Trends• Create new Classes• See where the matching

documents occur via Class Table

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Document/Page Viewer• Sorting Documents

• Most typical• Least typical

• View distinguishing terms• Representative use of

important words• Moving documents• Trend• Reports

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Keyword Class Generation• Execute->Classify by Keywords• Open queries (KCG files)

• One query per line• .AND. , .OR., (, )

• Add, Rename, Delete queries• Prioritize – Move up and down• Multiple/only one class• Ambiguous/first matching class• Run Queries• Save Queries• Run eClassifier

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Comparing Taxonomies• File->Compare Taxonomies

•File->Read Structured Information• Co-occurrence counts and affinities• Trend• View documents• Transpose• Report (CSV)

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Dictionary Co-occurrence• View->Dictionary Co-

occurrence• Type ahead searching• Co-occurrence counts and

affinities• Trend• View documents• Zoom in• Change Metric -> dependency

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Advanced Features• Visualization• Subclass from Structured Information• Make Classifier• Read Template• Import Category

• Add a category from another saved taxonomy

• Select Metrics• Add other columns to the Class table

• BIW

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Visualization• Look at relationships

between selected classes• Discover sub-clusters• Find “borderline”

examples• View/Move Documents• Navigator• Touring