Wong Cheuk Fun Presentation on Keyword Search

Head, Modifier, and Constraint Detection in Short Texts Zhongyuan Wang, Haixun Wang, Zhirui Hu

Popular iphone 5s smart cover Modifiers Constraint Head

90% of distinct queries consist of 2 or more components

Detection Challenges No grammar rules Popular iphone 5s smart cover vs Popular smart cover iphone 5s Require external knowledge Job search vs Job interview - Instance-level head-modifier knowledge - Conceptual knowledge - Concept-level head-modifier knowledge

Detection Approach (concept [head], concept [modifier], score) e.g. (accessary [head], device [modifier], 0.9) Three major challenges: - Knowledge’s coverage to handle all possible input - Avoid deriving conflicting patterns - Identify constraints from non-constraint modifiers

Mining Concept Patterns -- Probase IsA taxonomy - Entities vs concepts (Barack Obama) vs USA president 2.7 million concepts P(e|c) tells how popular e as concept c is concerned and vice versa. e.g. P(Fujitsu|Computer) > P(Acer|Computer) n(e,c) denotes the frequencies of e and c occur together

Mining Concept Patterns – Instance-level Head-Modifiers Identify head and modifiers no matter what their orders are “smart cover for iphone 5s” Other prepositions: - ‘of’, ‘with’, ‘in’, ‘on’, ‘at’ - When they are used, (A for B, A of B, A with B) it is almost always true that A is the head and B is the constraint.

Mining Concept Patterns – Concept-level Head-Modifiers Levels of Conceptualization (head, modifier, score)  (smart cover, iphone 5s) too specific, (obj, obj) too general  Conflicting rules: (company, device) vs (device, company) Conceptualizing instances 1. Map e to c if P(c|e) is among top k; 2. Map e to c if P(e|c) is among top k; 3. Map e to c if P(c|e)*P(e|c) is amont top k; 4. Map e to itself if e is itself a concept First two are not desirable as they are either too general or too specific For(3), larger value shows evidence of the closeness between c and e. For(4), we use entropy to identify popular instance:

Mining Concept Patterns – Conceptualizing Pairs Term “apple” conceptualizes to “fruit” or “company” “CEO for apple”  (CEO, fruit), (CEO, company) Obviously, (CEO, fruit) is wrong. Wrong concept pairs introduced will be filtered out due to low score

Head and Modifier Detection – Parsing 1. Text are parsed using Probase *“New York and New York Times” 2. Remove non-constraint modifiers 3. Cluster terms  Cluster short text having more than one head (e.g. apple ipad microsoft surface)  Reduce pair for conceptualization

Head and Modifier Detection for 2 components

Head and Modifier Detection for > 2 components Modifier can thus be ranked by its closeness to the head For query “college football player”, we remove the likely weakest edge college  player.

Mining non-constraint modifiers “Top query Seattle”, “good travelling hostel” Non-constraint modifiers: Top, good Non-constraint modifiers are more likely on the left of the query e.g. “cheap red shoe” instead of “red cheap shoe” Mining non-constraint modifiers using Probase  2.7 million concepts

Mining non-constraint modifiers – mining process 1. Construct modifier networks based on observations 2. Calculate score of each node as a non-constraint modifier in the networks Lower PMS makes it a non-constraint modifier

Framework for head, modifier and constraint detection

On Masking Topical Intent in Keyword Search Peng Wang and Chinya V. Ravishankar

Keyword-Based Obfuscation Hide real query in a mass of dummy queries generated using a Dummy Query Generation Algorithm (DGA). Advantage: Purely client-based Disadvantage: Not secure, cannot ensure real and dummy queries are indistinguishable

Topical Intent Obfuscation For a real user query q, dummy queries are created matching other topics. *Topic Relevance  ensure obfuscation Under two thresholds, α, β ( β < α ), with topic t and query q, Pr[t] : t’s relevance based on general interest pattern Pr[t|q] : t’s relevance after taking q into account Pr[t|q] - Pr[t] > α  t is relevant to q. Aim: Pr[t|q] - Pr[t] < β to create irrelevant dummy queries