Opportuni)es and Challenges of Textual Big Data for the Humani)es

Transcription

1 Opportuni)es and Challenges of Textual Big Data for the Humani)es Dr. Adam Wyner, Department of Compu)ng Prof. Barbara Fennell, Department of Linguis)cs THiNK Network Knowledge Exchange in the Humani)es RSA House, London, UK

2 Overview Introduc)ons. Big data resources. Text tools. Examples. Collabora)ve challenge. Knowledge exchange. 2

3 Big Data Technological, resource, and economic changes present opportuni)es and challenges to the humani)es. We live in a Big Data world of increasing bodies of textual data that are available on the Internet from libraries, government organisa)ons, social websites, and blogs. 3

4 The Story is Out There Big Data analysis in the news (since 2008): Obama's Open Government Law PRISM. h\p:// Mayer- Schonberger and Cukier (2013). Big Data. Foreign Affairs. "The Rise of Big Data". What are the consequences of leaving the tools in the hands of large organisa)ons with social/commercial interests? 4

5 Big Data Lots being done in: bioinforma)cs (searching ar)cles to 'link up' knowledge). legal patent analysis (newness). commerical text mining (corporate blogs, Amazon, Facebook, Thomson- Reuters NER, etc). security services. medical records. 5

6 Samples Open Source Data Open government data (UK, US, EU). Library collec)ons that are out of copyright. Corpora, e.g. Public.Resource.Org, Legal Informa)on Ins)tutes, others... Blogs, websites, open websites, open journals, communica)ons... English and other languages. Value and benefits of text mining - JISC 6

7 Current Prac)ce and Future Direc)on Current Big Data prac)ce of working with the meta- data, explicit network informa)on (e.g. linking friends to friends), or databases. Contrast with informa.on extrac.on from text. Sen)ment (posi)ve and nega)ve views) analysis is being done, but coarse- grained. How about fine- grained textual content analysis? 7

8 Some Research Ques)ons From 1641 Deposi)ons: What is a deposi)on (commonali)es across text)? How is hearsay defined (how does it appear)? How did the deposi)ons change over )me? What are the interrela)onships between deposi)ons in terms of the content? How is evidence manipulated by third par)es (what are the textual indicators across text)? 8

9 Some Research Ques)ons From Statutes and Regula)ons: What are networks of laws? How did the statutes and regula)ons change over )me? What are the rela)onships between laws, business rules, and compliance? Cross jurisdic)onal varia)on in the realisa)on of statutes and regula)ons (disaster relief roles and ac)ons). 9

10 Tools Not only do we have new resources, but we have new and powerful tools to search, compare, accumulate, share, and represent informa)on about these data. 10

11 Outputs Network graphs showing rela)onships (references, links) between web- based material. Google's Ngram Viewer and Legal Language Explorer. 11

12 Graphs of Dutch Legal Document References Hoekstra, "A Network Analysis of Dutch Regula)ons 12

13 Google Ngram Viewer interstate commerce, railroad, right of way 13

14 Legal Language Explorer interstate commerce, railroad, right of way 14

15 Going Deeper Where Knowledge Ma\ers Going for structured seman)c informa)on contained in the texts. 15

16 Looking for? Named en)ty recogni)on (who, what, when, where). Coreference (associa)ng en))es across sentences and text). Fine- grained sen)ment analysis (posi)ve or nega)ve disposi)ons on par)culars). Word pa\erns (terminology that is descrip)ve). Seman)cally contenlul informa)on with annota)ons. Rela)onships, values,... 16

17 Tools h\p://en.wikipedia.org/wiki/text_mining General Architecture for Text Engineering 17

18 Sample Applica)ons Law (Legal case analysis, regula)on, argumenta)on). Psychological analysis (Phil Gooch), associa)ng pa)ent narra)ves with psychopathologies. An)- depressants, press, and influence (Nooreen Akhtar). 18

19 GATE Example on Argumenta)on Objec)ve: iden)fy and extract arguments from a corpus Web- based discussion forums on Amazon about cameras (papers by Wyner et al.). Could do this on the BBC's Have Your Say or similar. 19

20 Terminological Annota)ons Rhetorical structure informa)on (premise, conclusion, etc). Domain terminology (camera features, etc). Contrast (poorly, not, etc.). 20

21 Query for pa\erns 21

22 Structure and extract an argument for buying the camera Premises: The pictures are perfectly exposed. The pictures are well- focused. No camera shake. Good video quality. Each of these proper)es promotes image quality. Conclusion: (You, the reader,) should buy the CanonSX

23 Teamware Tool for distributed, collabora)ve seman)c annota)on. Makes a corpus searchable by seman)c concepts. Collec)ve introspec)on - making subjec)ve evalua)ons objec.ve, comparable, measurable, generalisable, and retestable. 23

24 Teamware Example Crowdsourced Legal Case Annota)on, Wyner Like manual annota)on, but online and automa)cally compared. Can create online, collabora)ve annota)on tasks for lots of text and concepts argumenta)on, story roles, newspaper elements, poli)cal posi)ons,... 24

25 Collabora)ve Challenge The challenge is to develop not only the tools, which we largely have to hand, but more importantly the human resources to work with them to carry out distributed, collabora)ve projects. 25

26 Collabora)ve Challenge The interface computer people bring x, humani)es people bring y, combined they produce z. Specialist knowledge is built into something that is machine processable, e.g. lists and rules in GATE. Collabora)vely building gold standards; refining the lists and rules. 26

27 Knowledge Exchange Purng tech in hands of humani)es scholars. Team collabora)on in development humani)es scholars provide their subject specific knowledge; tech provide tools, support, development, frameworks, analysis. Crea)ng, growing, and maintaining a common language. Lawyers, Linguists, Arts, Social/Poli)cal Scien)sts, Policy- makers... Specific tools, how- tos, sta)s)cs, auxiliary coding... 27

28 Other Tools for Various Users Other tools to explore 'Mining the Social Web', data mining, visual analy)cs... Variety of users with different skill levels. 28

29 Thanks for your a\en)on! Ques)ons? Contacts: Adam Wyner Barbara Fennell 29