Annotating, Extracting, and Linking Legal Information
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1 Annotating, Extracting, and Linking Legal Information Adam Wyner University of Aberdeen Department of Computing Science University of Edinburgh Law School March 11, 2014
2 Outline Background, context, materials. Samples. Wider and deeper in content analysis. Question for you: How could you mix into legal informatics? 11/03/2014 Wyner - Edinburgh Law School 2
3 Background, Context, Materials 11/03/2014 Wyner - Edinburgh Law School 3
4 My Background AI and Law since 2001 Linguistics PhD (Cornell, 1994) and Computer Science PhD (King's College London 2008). Contract concepts in a logic and a computer program. Textual information extracted from legal cases, legislation, and regulations. Legal case ontologies. Legal argumentation in case law and judicial hierarchy. Policy-making support tools. LegalRuleML machine readable mark up of the law. 11/03/2014 Wyner - Edinburgh Law School 4
5 Semantic Web of the Law Want to semantically annotate, extract, and link meaningful content in a corpus of legal documents, not just retrieve documents (a la Google). Use information to reason, e.g. input data to rules. Use information to link to other documents with related information, e.g. legislation on a certain topic or other web-based documents. Building services on top of annotated data for search, comparison, reuse, mash-ups. 11/03/2014 Wyner - Edinburgh Law School 5
6 State of Play Lots of Change Internet. Government information needs (transparency, efficiency...). Open Data and Big Data. Making the law machine-readable (UK and USA). More powerful search and reasoning tools. Economic change in firms, billing, hiring... (Susskind). Stanford's CodeX, ReInventLaw, Harvard's Berkman. Blogs: List of projects (at end). 11/03/2014 Wyner - Edinburgh Law School 6
7 Other Projects/Links openlaws.eu (Radboud Winkels) Court of Cassation (Annie Foret) Text analysis for legal histories (Adam Wyner). Also see: Old Bailey, Trading Consequences, 1641 Depositions,... Text mining cases (Wyner, Ashley) IAAIL, JURIX, Journal of AI and Law Casetext, other legal-tech startups Harvard's Berkman Center H20 project 11/03/2014 Wyner - Edinburgh Law School 7
8 Set the Context Support tools for information extraction and decision support. Formal analysis of the law and legal system. Decompose problems; incrementally create partial solutions. Individual v Collaborative, Collective, distributed legal work. Not robot lawyers, or fully automated justice, or 3 Laws. Law Applied to Computers privacy, software patents, data management, copyright... Computers Applied to Law knowledge extraction, representation, management; reasoning; networks. 11/03/2014 Wyner - Edinburgh Law School 8
9 Materials Legislation, regulation, guidance, case law, bankruptcy notices, responses to policy making consultations, documents recording practice knowledge,... A main reason for recent developments in legal informatics internet/semantic web ideology open legal data. 11/03/2014 Wyner - Edinburgh Law School 9
10 Samples 11/03/2014 Wyner - Edinburgh Law School 10
11 Various The Stationery Office's OpenUp Data Enrichment Service Google Scholar links case citations and legislation Annotating structure of law UK Legislation Akoma Ntosa and LegalRuleML Google ngrams Networks of linked citations Oracle Policy Automation 11/03/2014 Wyner - Edinburgh Law School 11
12 The Stationery Office (TSO) Magic Pop some text into the website. Pick your output type (default semantic annotations, RDF, XML ). Get your output. A mash-up. How can we do the same for our data in our own way? 11/03/2014 Wyner - Edinburgh Law School 12
13 openup.tso.co.uk/openup-platform 11/03/2014 Wyner - Edinburgh Law School 13
14 759 F2d 1053 Defiance Button Machine Company v. C & C Metal Products Corp 11/03/2014 Wyner - Edinburgh Law School 14
15 Named entity recognition 11/03/2014 Wyner - Edinburgh Law School 15
16 11/03/2014 Wyner - Edinburgh Law School 16
17 11/03/2014 Wyner - Edinburgh Law School 17
18 11/03/2014 Wyner - Edinburgh Law School 18
19 Google's Searches in Case Law Public Resource 11/03/2014 Wyner - Edinburgh Law School 19
20 Scoped to Jurisdiction 11/03/2014 Wyner - Edinburgh Law School 20
21 Linked Data 11/03/2014 Wyner - Edinburgh Law School 21
22 Linked Results 11/03/2014 Wyner - Edinburgh Law School 22
23 How Cited 11/03/2014 Wyner - Edinburgh Law School 23
24 Go to Passage In Boggs v Boggs 11/03/2014 Wyner - Edinburgh Law School 24
25 Legislation UK Harper CATO. UK Legislation Annotation National Archives 11/03/2014 Wyner - Edinburgh Law School 25
26 Legislation UK Harper CATO. UK Legislation Annotation National Archives 11/03/2014 Wyner - Edinburgh Law School 26
27 LegalRuleML 11/03/2014 Wyner - Edinburgh Law School 27
28 LegalRuleML 11/03/2014 Wyner - Edinburgh Law School 28
29 Legal Language Ngram Explorer interstate commerce, railroad, right of way 11/03/2014 Wyner - Edinburgh Law School 29
30 Graphs of Dutch Legal Document References Hoekstra, "A Network Analysis of Dutch Regulations" 11/03/2014 Wyner - Edinburgh Law School 30
31 Oracle Policy Automation Industrial scale applications with Oracle Policy Automation: Scope legislation and regulation, express policies and knowledge base in a "just enough" controlled natural language, served as web-based business/policy system. Creating Rules in Oracle Policy Automation creating-rules-in-oracle-policy-automation/ UK DirectGov Benefits Calculator DoItOnlineByCategory/DG_ Rules representation and reasoning using web-based natural language interfaces. Why no similar open-source, academic tool? 11/03/2014 Wyner - Edinburgh Law School 31
32 Oracle Policy Automation 11/03/2014 Wyner - Edinburgh Law School 32
33 Oracle Policy Automation Language is 'normalised', parsed, semantically represented for inference 11/03/2014 Wyner - Edinburgh Law School 33
34 Oracle Policy Automation 11/03/2014 Wyner - Edinburgh Law School 34
35 Wider and Deeper 11/03/2014 Wyner - Edinburgh Law School 35
36 Rule Extraction 11/03/2014 Wyner - Edinburgh Law School 36
37 Rule Extraction Want to extract rules from legislation and regulations to make rule books, process further (executables), linking rules to one another, linking rules to parties, external sources,... Uses GATE. 11/03/2014 Wyner - Edinburgh Law School 37
38 Rule Extraction Blood Regulations Consequence, list structure, and conjuncts of the antecedent. Exception, agent NP, deontic concept, active main verb, theme. 11/03/2014 Wyner - Edinburgh Law School 38
39 Collaborative Annotation for Legal Education 11/03/2014 Wyner - Edinburgh Law School 39
40 Collaborative Annotation for Legal Education LCBR - common law contexts, where lawyers argue for an decision in a current case on the basis of precedents (previous decisions), comparing and contrasting the cases in terms of the facts of the cases and the legal rules that are applied. Would like to annotate, extract, and link the various parts of the cases internally (links between cases or from cases to legislation) and externally (information outside the case base or legislation). 11/03/2014 Wyner - Edinburgh Law School 40
41 Students Annotating and Discussing Cases Create a gold standard corpus. Manually annotated with an online tool. Some components can already be automatically annotated. Grow corpus. Analyse components, e.g. what are the linguistic indicators legal fact, holding, rationale, decision, etc? Apply some Machine Learning (once we have the corpus). 11/03/2014 Wyner - Edinburgh Law School 41
42 Elements Annotated 11/03/2014 Wyner - Edinburgh Law School 42
43 Tool I 11/03/2014 Wyner - Edinburgh Law School 43
44 Results of Annotation The annotators carry out their task and complete the project. Carry out inter-annotator agreement analysis. Curate the disagreements to create a Gold Standard corpus. Use this for machine learning. Search the annotations using an online tool, e.g. ANNIC. 11/03/2014 Wyner - Edinburgh Law School 44
45 Searching Annotations 11/03/2014 Wyner - Edinburgh Law School 45
46 Parsing and Logically Representing Legislation 11/03/2014 Wyner - Edinburgh Law School 46
47 Executable Rules and XML Natural language: If a person satisfies the conditions then the secretary must register her. Logic (inference, consistency): Forall-x [Exists-y [person(x) & the-conditions(y) & satisfy(x,y)] -> register(the-secretary,x)] Prolog (executable): register(the-secretary,x) :- person(x), theconditions(y), satisfy(x,y). XML (transmittable, mutable), e.g. RuleML: 11/03/2014 Wyner - Edinburgh Law School 47
48 C&C/Boxer C&C/Boxer Groningen Meaning Bank 11/03/2014 Wyner - Edinburgh Law School 48
49 C&C/Boxer Lexically given grammar (how words combine). Uses a statistical model to output the most likely parse. Successful wide-coverage (of the Penn Treebank corpus). Parses and semantically represents more than Attempto Controlled English. Translates to DRS (FOL). Nutcracker inference tool (textual entailment tool). Translation to RuleML. Highlights issues of interpretation. 11/03/2014 Wyner - Edinburgh Law School 49
50 C&C/Boxer Examples Cameron is a British citizen. If an application is made to register as a British citizen a person who is a British overseas territories citizen, the Secretary of State may, if he thinks fit, cause the person to be so registered. 11/03/2014 Wyner - Edinburgh Law School 50
51 Parse - Cameron is a British citizen 11/03/2014 Wyner - Edinburgh Law School 51
52 DRS - Cameron is a British citizen 11/03/2014 Wyner - Edinburgh Law School 52
53 FOL-XML - Cameron is a British citizen 11/03/2014 Wyner - Edinburgh Law School 53
54 Larger Example If an application is made to register as a British citizen a person who is a British overseas territories citizen, the Secretary of State may, if he thinks fit, cause the person to be so registered. 11/03/2014 Wyner - Edinburgh Law School 54
55 11/03/2014 Wyner - Edinburgh Law School 55
56 Output Analysis 11/03/2014 Wyner - Edinburgh Law School 56
57 Modals Alethic (necessity,...), Epistemic (know,...), Temporal, Deontic (must, may,...). Subclasses of modal operators and propositional attitudes with different inference properties. Why are these not relations rather than sentential operators? Factive know, saw, heard,... Non-factive believe, remember (?),... Contrafactive wish, imagine,... Epistemic-root interpretation may, must, can,... Scope issues with de re/de dicto. 11/03/2014 Wyner - Edinburgh Law School 57
58 Issues Legal rules are defeasible what is the linguistic and formal representation of this? 11/03/2014 Wyner - Edinburgh Law School 58
59 Thanks for your attention! Contact: Adam Wyner, Comments, questions, suggestions... 11/03/2014 Wyner - Edinburgh Law School 59
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