Anotaciones semánticas: unidades de busqueda del futuro?

Transcription

1 Anotaciones semánticas: unidades de busqueda del futuro? Hugo Zaragoza, Yahoo! Research, Barcelona Jornadas MAVIR Madrid, Nov.07

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3 Document Understanding Cartoon our work! Complexity of Document Understanding grep search engines Q&A semantic web? domain expert

4 Beyond strings, beyond bag of words In the room the women come and go Talking of Michelangelo. { ARTIFACT, PEOPLE, VERB MOTION, VERB COMMUNICATION, PERSON } { room, women, come, go, talking, Michelangelo } { ARTIFACT room --MODIFIER, PEOPLE women --SUBJECT, MOTION come and go --VERB, } { COMMUNICATION talking --VERB, PERSON Michelangelo --OBJECT } } Applications in Search, Algorithmic Advertisement, Answers, { PEOPLE MOTION, COMMUNICATION PERSON, Michelangelo }

5 Beyond strings, beyond bag of words In the room the women come and go Talking of Michelangelo.

6 Structure & Domain Knowledge Domain Verticals Independent Specialised Search WWW RSS Feeds Blogs News Mail Y! Answers Health Search Tasks: Find relevant to string. Methods: String matching (tf-idf) Hyperlink Popularity Domain Dependent Craig s List Ryanair InfoZoom FaceBook Tasks: AirFlight Booking (find flights) Housing (find apartment) Hire People (find CVs) Methods: Match + Domain-Based Ranking

7 The NLRA Pipeline Segmentation, Tokenisation POS Word Named Semantics Entities Dependency Parser SuperSense Tagger Open Source Kryptonise, Pigcise, Corpus Adaptation Sentiment Analysis (example) Gazeteers Code Docum. & Support Corpus Adaptation Anaphora Resolution Pipeline Server Multilingual Support

8 Statistical Methods for Semantic Tagging NU LL I-P ER SO N BPE RS ON CA RD IN AL Sequence bracketing task: Model: Collins Parser (Avg.Perceptron-HMM tagger) Features: tokens, POS, word shape, most frequent, previous label, combinations. (Massimiliano Ciaramita and Yasemin Altun, EMNLP 2006)

9 Example: WordNet Supersense labels

10 Extraer Entidades

11 Statistical Methods for Semantic Tagging Method Recall Rand Baseline Supersense-Tagger Precision F-score (Massimiliano Ciaramita and Yasemin Altun, EMNLP 2006)

12 Semantic Web & User annotations Microformat

13 Semantic Web & User annotations Categories, lists

14 NLRA Search Engine query Fast inverted index (LUCENE): normalized text, (IXE) unit of retrieval. TAG-aware Forward index (UIMA): surface and normalised text, POS, overlapping semantic tags resolved anaphoras, Inverted Index docid docid unit Id Forward Index (demo) Feature extractor Id, score Id, Id,score score

15 NLRA Server Corpus Pipeline Index Forward Index Tag Graph Your Killer Application! Search Engine (C++, IXR) Graph Engine (Java, WebGraph) NLR Search Engine RMI & REST APIs

16 Lenguaje Escrito

17 Applications II: Better Operators

18 Entity Extractor + Dependency Extractor Type: LOCATION PEOPLE ORGANIZATION DATE Anchor: Role: SUBJECT OBJECT MODIFIER Target: VERB SUBJECT OBJECT MODIFIER Sentiment Analisys Informational Generic/Specific Objective/Subjective Positive/Negative

19 The problem: ranking (very many) (typed) entities Millions of unique entities, dozens of types: 1 model per entity is unfeasale we explore on-line (ad-hoc) models [Zaragoza et.al. CIKM07]

20 Task examples

21 SW0: a publically available Semantic Snapshot of Wikipedia 6.2K English Wikipedia entries. Sentence and token splitting POS, NEs, Semantic tagging (WNSS). 28M unique entities, 5.5M occurrences. Dependency Parsing. link

22 Entity Containment Graph query Wikipedia search Sentences

23 Lenguaje Escrito

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26 Problems Descriptive entities are not as interesting: person vs. Picasso city vs. Paris Easy fix: discount by global entity frequency: But it d be better to introduce types: Bush > food!

27 Refining Entity Graphs Different type semantics

28 Detailed Results

29 Web-RankDiscounted method QUERY = Life of Pablo Picasso WWW search ENTITY = Gertrude Stein Gertrude Stein ENTITY_RANK Life of Pablo Picasso QUERY_SET Score(ENTITY, QUERY) = AvgPrec [ rank=entity_rank, rel=query_set ]

30 Detailed Results

31 Gracias,