Presentation on theme: "Automating Creation of Hierarchical Faceted Metadata Structures Emilia Stoica, Marti Hearst and Megan Richardson* School of Information, Berkeley *Dept."— Presentation transcript:
Automating Creation of Hierarchical Faceted Metadata Structures Emilia Stoica, Marti Hearst and Megan Richardson* School of Information, Berkeley *Dept. of Mathematical Sciences, NMSU
Focus: Browse Large Datasets Standard search interface - query box + retrieved results – not suited for browsing and navigation User interfaces need to group and organize the results
How do we Create Faceted Hierarchies? Goals: Help an information architect to create the hierarchy Currently they do it all by hand! Balance depth and breadth Avoid “skinny” paths Don’t go too deep or too broad Choose understandable labels Disambiguate between word senses
Related Work Automated text categorization LOTS of work on this Assumes that a set of categories is already created Little if any work on building facet hierarchies
Castanet Carves out a structure from the hypernym (IS-A) relations within WordNet Semi-automatic algorithm for creating hierarchical faceted metadata Produces surprisingly good results for a wide range of subjects e.g., recipes, medicine, math, news, fine arts image description
WordNet Challenges A word may have more than one sense - Fine granularity of word sense distinctions e.g., newspaper (#1) - daily publication on folded sheets newspaper (#3) - physical object - Ambiguity for the same sense tuna #1 cactus #2 fish food fish bony fish
WordNet Challenges (cont.) The hypernym path may be quite long (e.g., sense #3 of tuna has 14 nodes) Sparse coverage of proper names and noun phrases (not addressed)
Our Approach Documents Select terms WordNet Build core tree Augment core tree Remove top level categories Compress Tree Divide into facets
1. Select Terms Select well-distributed terms from the collection Eliminate stopwords Retain only those terms with a distribution higher than a threshold (default: top 10%) Documents WordNet Select terms Build core tree Comp. tree Remove top level categ. Augm. core tree
2. Build Core Tree Get hypernym path if term: - has only one sense, or - matches a pre-selected WordNet domain Adding a new term increases a count at each node on its path by # of docs with the term. frozen dessert sundae entity substance,matter nutriment dessert ice cream sundae frozen dessert entity substance,matter nutriment dessert sherbet,sorbet sherbet Build a “backbone” Create paths from unambiguous terms only Bias the structure towards appropriate senses of words Documents WordNet Select terms Build core tree Comp. tree Remove top level categ. Augm. core tree
3. Augment Core Tree Attach to Core tree the terms with more than one sense Favor the more common path over other alternatives Documents WordNet Select terms Build core tree Comp. tree Remove top level categ. Augm. core tree
Augment Core Tree (cont.) Date (p1) Date (p2) entity abstraction substance,matter measure, quantity food, nutrient fundamental quality nutriment time period food calendar day (18) edible fruit (78) date Sunday berries date Choose this path since it has more items assigned ? ?
Optional Step: Domains To disambiguate, use Domains Wordnet has 212 Domains medicine, mathematics, biology, chemistry, linguistics, soccer, etc. A better collection has been developed by Magnini (2000) Assigns a domain to every noun synset Automatically scan the collection to see which domains apply The user selects which of the suggested domains to use or may add own Paths for terms that match the selected domains are added to the core tree
Using Domains dip glosses: Sense 1: A depression in an otherwise level surface Sense 2: The angle that a magnet needle makes with horizon Sense 3: Tasty mixture into which bite-size foods are dipped dip hypernyms Sense 1 Sense 2 Sense 3 solid shape, form food => concave shape => space => ingredient, fixings => depression => angle => flavorer Given domain “food”, choose sense 3
4. Compress Tree Rule 1: Eliminate a parent with fewer than k children unless it is the root or its distribution is larger than 0.1*max dist ice cream sundae dessert sundae frozen dessert sherbet,sorbet sherbet parfait dessert frozen dessert sundae parfait sherbet abstraction Documents WordNet Select terms Build core tree Comp. tree Remove top level categ. Augm. core tree
4. Compress Tree (cont.) Rule 2: Eliminate a child whose name appears within the parent’s name sundae dessert frozen dessert parfait sherbet dessert sundaeparfaitsherbet abstraction Documents WordNet Select terms Build core tree Comp. tree Remove top level categ. Augm. core tree
5. Divide into Facets Divide into facets
5. Divide into Facets (Remove top levels) sugar syrup entity substance,matter food,nutriment ingredient,fixings food stuff,food product sweetening herb flavorer parsley oregano sugar syrup sweetening herb flavorer parsley oregano Rule 1: Eliminate the top t levels (t =4 for recipe collection). Divide into facets Rule 2: For each resulting tree, test if it has at least n children (n =2) If yes, stop. If not, delete the root and repeat. Manual cleaning: remove facets that don’t make sense
Example: Recipes (13,500 docs)
Castanet Output (shown in Flamenco)
Castanet Evaluation This is a tool for information architects (IA), so people of this type did the evaluation Each IA compared Castanet to other state-of- the-art algorithms LDA (Blei et al. 04) Subsumption (Sanderson & Croft ’99) Baseline: most frequent terms in the collection Datasets 13,000 recipes from Southwestcooking.com
Evaluation Method For each of 2 systems’ output: Examined and commented on top-level Examined and commented on two sub-levels Then comment on overall properties Meaningful? Systematic? Likely to use in your work? L C S C } 16 } 18
Evaluation (cont.) Sample questions for top level categories: - Would you add/remove/rename any category ? - Did this category match your expectations ? Sample questions for a specific category: - Would you add/move/remove any sub-categories ? - Would you promote any sub-category to top level ? General questions: - Would you use Castanet ? - Would you use LDA ? - Would you use Subsumption ? - Would you use list of most frequent terms ?
Evaluation Results “Would you use this system in your work?” “yes definitely”, “yes, in some cases” Castanet 85% LDA 0 % Subsumption 37% Baseline74% Average response to questions about quality (4 = “strongly agree”, 3 = “agree somewhat”, 2 = “disagree somewhat”, 1 = “strongly disagree”)
Evaluation Results Average responses for top-level categories (4= “no changes”, 3 = “one or two”, 2 = “a few”, 1 = “many”) Average responses for 2 subcategories
Needed Improvements Take spelling variations and morphological variants into account Use verbs and adjectives, not just nouns Normalize noun phrases Allow terms to have more than one sense Improve algorithm for assigning documents to categories.
Conclusions Flexible application of hierarchical faceted metadata is a proven approach for navigating large information collections. Midway in complexity between simple hierarchies and deep knowledge representation. Currently in use on e-commerce sites; spreading to other domains Systems are needed to help create faceted metadata structures Our WordNet-based algorithm, while not perfect, seems like it will be a useful tool for Information Architects.
Conclusions Castanet builds a set of faceted hierarchies by finding IS-A relations between terms using WordNet. The method has been tested on various domains: medicine, recipes, math, news, description of images Usability study shows: Castanet is preferred to other state-of-the art solutions. Information architects want to use the tool in their work. Future work Apply to tags (flickr, delicious)
Learn More Funding This work supported in part by NSF (IIS ) For more information: Stoica, E., Hearst, M., and Richardson, M., Automating Creation of Hierarchical Faceted Metadata Structures, NAACL/HLT 2007 See