Meaning Banking and Beyond
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1 Meaning Banking and Beyond Valerio Basile Wimmics, Inria November 18, 2015
2 Semantics is a well-kept secret in texts, accessible only to humans. Anonymous I BEG TO DIFFER
3 Surface Meaning
4 Step by step analysis Dividing text into words
5 Step by step analysis Dividing text into words Labeling words
6 Emotions play an important role in p. noun verb det adjective noun prep decision making noun noun
7 Step by step analysis Dividing text into words Labeling words Finding links between words
8 Emotions play an important role in decision making subj obj attr nn
9 Step by step analysis Dividing text into words Labeling words Finding links between words Extracting the meaning
10 Emotions play an important role in decision making play(emotion, role) important(role) emotion >1...
11 Step by step analysis Dividing text into words Tokenization Labeling words Tagging Finding links between words Syntax Extracting the meaning Semantics
12 Software pipeline supervised Tokenization Elephant (Evang et al. 2013) Tagging supervised C&C tools (Curran et al. 2007) Syntax Semantics Boxer (Bos, 2008) rule-based
13 Emotions play an important role in decision making
14 Supervised learning Show enough examples to the machine and it will learn and generalize. As opposed to unsupervised learning, e.g. clustering
15 Supervised learning enough examples = millions of words + annotation that is, an annotated text corpus Examples: Penn treebank (syntax) Semcor (word senses) TWITA (sentiment),...
16 What about a semantics?
17 What about a semantics? Meaning Bank (a treebank for semantics)
18
19 How to build a meaning bank Manually: Collection of texts + Expert annotation
20 How to build a meaning bank Manually: Collection of texts + Expert annotation DO NOT TRY THIS AT HOME
21 How to build a meaning bank By bootstrap: Collection of texts + Analysis software + Manual correction
22 Collection of texts English Public domain Whole documents Short Open domain
23 Collection of texts 73,352 documents 10,103 (accepted) 6.3 sentence per document ~90% from VoA (newswire) Jokes, legal text, fables,...
24 Analysis software GNU Make Elephant tokenization Daemon process C&C tools taggins & parsing Boxer Semantic analysis
25 Manual correction Silver standard Experts and the crowd
26 The GMB Explorer
27 The GMB Explorer 33 users 173,173 annotations (including automatically generated)
28 Gamification
29 Gamification Leaderboards Badges Agreement-based score
30 Gamification 59,413 Answers 13 Games 1,732 Players 2 Datasets
31 A semantically annotated resource that anyone can edit. Johan Bos, Valerio Basile, Kilian Evang, Noortje Venhuizen, Johannes Bjerva (forthcoming): The Groningen Meaning Bank. In Handbook of Linguistic Annotation. Berlin: Springer. Valerio Basile, Johan Bos, Kilian Evang, Noortje Venhuizen Developing a large semantically annotated corpus. LREC 2012 Valerio Basile, Johan Bos, Kilian Evang, Noortje Venhuizen A platform for collaborative semantic annotation. EACL
32 Beyond Meaning Banking
33 Beyond Meaning Banking Autonomous learning of the meaning of objects
34 Beyond Meaning Banking Autonomous learning of the meaning of objects
35 Beyond Meaning Banking Bridging semantic analysis with entity linking to build a knowledge base by reading the Web
36 Beyond Meaning Banking When a glass or cup is emptied, The robot will ask if it should serve more serving (FrameNet) Agent Robot (DBPedia) Patient Glass (DBPedia)
37 Towards a Web Meaning Bank Better NLP tools train extract Better Linked Open Data
38 Fin Meaning Banking and Beyond Valerio Basile November 18, 2015
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