Transforming the Representation of Lexical Knowledge

1. Transforming the Representation of Lexical Knowledge Christopher Manning University of Sydney http://www.sultry.arts.usyd.edu.au/cmanning/

2. Project Objectives Aims of the project: • examining the richness of lexical structure, in particular the connotational and figurative use of words • providing innovative ways for representing a dictionary, through creative use of the medium of computers • augmenting dictionaries from corpora • to be able to provide practical educationally useful programs as a result (at low labor cost) Main initial target: an interactive front end for exploring or using the Warlpiri dictionary.

3. Acknowledgements • Ken Hale, Mary Laughren, Jane Simpson, Robert Hoogenraad, David Nash, Kay Ross • Kevin Jansz, Nitin Indurkhya, Katrina Avila • Susan Poetsch, Miriam Corris, John Henderson • (and many others)

4. Talk Outline • The research agendas • Dictionary usability and usefulness • Kirrkirr: A Warlpiri dictionary browser • Underlying data • User interface and visualization • Corpus enrichment for terminology sets • User study

5. Research Program: Lexicon • A lexicon is not just words but a vast network of associations between words and within and across the concepts represented by words • The aim of this work is to provide people with a better understanding of this conceptual map. • E.g., patterns of figurative extension: in a song about a stockman driving a car ‘glass’ is used first for the windscreen, and then metaphorically for ‘sexual attraction’, using a systematic pattern of figuration between shining and sex

6. Lexicon (cont) • Traditional paper dictionaries offer very limited ways for making such networks visible • On a computer, one can imagine all sorts of ways of bringing out such relationships

7. Research: Computational Lexicography • Dictionaries on computers are now commonplace • But there has been little attempt to utilize the potential of the new medium • Goal: fun dictionary tools that are effective for language learning, browsing, and research • Special interest: dictionaries for minority languages. Here economic, motivational, and user support reasons all point to an important role for computers.

8. MRD Structure • The internal structures of current Machine Readable Dictionaries usually merely mimic the structure of the printed form (Boguraev 1990) • Some work, notably WordNet (Miller 1995) has involved a fundamental rethinking of dictionary content and organization (here, organization via “synsets” which are related via links of part, subkind, opposite) • But this research hasn’t been taken to users.

9. Research Program: Education • Dictionary structure and usability are often dictated by professional linguists, while the needs of others (speakers, semi-speakers, young users, second language learners) are not met • Weiner (1994) : The initial purpose of the OED: • “to create a record of vocabulary so that English literature could be understood by all. But English scholarship grew up and lexicography grew with it … inevitably parting company with the man in the street.” • Challenge is to avoid this.

10. Dictionary usefulness and usability Kegl (1995) “Machine-Readable Dictionaries and Education” • “Originally, this paper was intended as a survey of educational applications using MRDs. As far as I have been able to determine, no such applications currently exist” • Standard dictionaries are reference works, ill-suited for use as learning tools • Studies of American ‘dictionary skills training’ show that many tasks achieve little in the way of education (but do teach word lookup!)

11. Educational value of dictionaries However derived lexical information is useful! Think of a high school foreign language textbook • terminology sets • pictures with parts named • vocabulary lists • word explications Major issue: • Not many people sit around reading dictionaries – need something fun

12. Data on usability: evaluating a paper dictionary • Study of paper dictionary usability by Susan Poetsch, tested using Alawa dictionary (draft by Margaret Sharpe) • In community, old people are very concerned to keep language strong, and help as volunteers in bilingual education. They are keen on dictionary • However, they lack the literacy skills to use it • Susan worked with people aged 25–50 • Since volunteers, probably better than average literacy skills for the community

13. Findings • Not very literate: A big dictionary is overwhelming to someone with emerging literacy skills • People knew words are ordered but could not use ordering effectively (restart or flick randomly) • Often around 3 minutes a word lookup • People lost place in page regularly • An overcrowding of information is confusing • One word correspondences are easiest for users, but often unrealistic linguistically • Subentries were confusing; part of speech puzzling

14. Findings (2) • Regular dictionary users (especially, compilers!) grossly underestimate the time they have spent becoming familiar with dictionary structure • If a dictionary is going to be made for a speech community, then the people in that community need to feel confident in using it. • Teachers felt that the draft dictionary is too long and detailed for school use • Conclusion: These people need a different dictionary (My First Alawa) • Would probably be used by adults as well as kids

15. Initial focusKirrkirr: a Warlpiri browser • Warlpiri is an Australian Aboriginal language spoken in the Tanami desert (NW of Alice) • A computer interface for browsing the Warlpiri dictionary. • Rich lexical materials have been collected by linguists over decades (Hale’s fieldwork from 1959 on, MIT Lexicon Project in the 1980s) • The results still haven’t been produced in a format usable by the community (only printouts) • Previous computer projects have faltered

16. Past Problems • “At least 15 years have passed during which the Warlpiri dictionary could have been tested, people trained in dictionary use, and the dictionary improved with user input, but all that has been produced is one badly formatted raw paper printout” • Huge amounts of human labor have been expended • Information systems 101: need to deliver, and provide the kind of process automation to make production and revisions easy

17. Our educational goals • Aim at school kids • “Information seeking is a complex process which is often not attended to in K-12 education” (Wallace et al. 1998) • Provide learner supports for getting started with dictionaries • Adaptable interface: can cater to different needs • Support for active reading by allowing note taking • An interface where you can see words, but are not required to know words

18. Target user community

19. Kirrkirr: A Warlpiri dictionary browser (Jansz 1998; Jansz, Manning and Indurkhya 1999) • An environment for the interactive exploration of dictionaries. • The design is general, but our current work has just been with Warlpiri (Arrernte coming soon!) • Attempts to more fully utilize graphical interfaces, hypertext, multimedia, and different ways of indexing and accessing information • Written in Java, it can either be run over the web [high bandwidth] or run locally (here Java’s main advantage is cross-platform support).

20. Specific goals • An interactive environment that encouraged exploration: easy and fun to use • Reduction of the dependence on alphabetical order • Catering to the needs of different user groups (kids, teachers, professionals) • Flexible enough to display appropriate information in appropriate ways depending on user level

21. Overview Kirrkirr provides various modules • Graph layout of word relationships • Formatted dictionary entries • Semantic domain browsing • A notes facility for ‘jotting in the margin’ • Multimedia: audio, pictures • Advanced searching interfaces • others in planning: colors, figuration patterns These attempt to cater to users with different competence levels

23. The lexical database • Existing materials are stored in an ad hoc format of markup using backslash codes with some (rather odd) nesting of structural tags • These were converted to XML using an error-correcting stack-based parser (written in PERL). • The inconsistency and flexibility of dictionary entries actually made this a surprisingly difficult task. • But parser tries to impose data integrity • Use of XML gives a clear structure to the data, and makes available many (free) tools

24. XML • XML: a descendant of SGML for structured markup of text • XML separates the structure of the data from its presentation • Much of the recent enthusiasm for XML has centered around representing simple and rigid structures such as database records • The rich hierarchical and variable structure of dictionary entries is really more what something like XML excels at! • Result remains a portable, tangible text file

25. Alternative: a database • The obvious thing for storing a lot of data • Has clear advantages: structure, indexing, query language, relationships, integrity. • Many people have suggested using a database for lexical data and some have actually done it (IITLEX, Austin and Nathan) • But in general lexicographers oppose the rigidity, and, in practice, standard relational databases are quite ill-suited to dictionaries

26. Dictionary entries vary enormously: word cross-reference word POS gloss example translation word dialects [sense-1 POS1 definition gloss example translation example translation] [sense-2 POS2 dialect definition example translation subentry-word gloss synonym] etymology Data is fragmented Same element can appear at many levels (dialect, crossreference, …) Dictionaries are only loosely structured Database model is inflexible to extending the dictionary structure Lessens portability [Answer: an object database] Problems

27. XML indexing • XML is a median between the structure, indexing, etc. of a database, and the freedom of a word processor. • To improve speed, an ad hoc index to the XML file is built, and can be used for rapid headword and gloss lookup and indexing which parts of the XML file to process.

28. Visualization of dictionary information • For applications with simple textual content behind them, there is little that can be done but an on-line reflection of a printed page • But we want more than just definitions of words: we want to know their relationships to other words, and the patterning in these relationships • In a computational approach, can mediate between the lexical data and the user • The interface can select from and choose how to present information (according to the user’s preferences) – in many different ways

29. Previous work • Current systems present the search-dominated interface of classic Information Retrieval systems: you type a word in a search box • Results try to mimic, but are generally inferior to, the printed version of the dictionary • Good feature: rapid searching • These systems do little to utilize the captivating qualities of computers: interactivity, user control and adaptability (Brown 1985).

30. Previous work (2) • Only effective when user has a clearly specified information need – even here, we are ignoring the distinction between information gained and knowledge sought (Sharpe 1995) • Lack browsing, and chances for incidental or curiosity driven learning • Lack tangibility and situatedness of paper: ineffective for getting an idea of a collection • We wish to exploit the essence of hypertext, which is “click to explore” browsing

31. Previous work (3) • Little research work (in corpus linguistics, visualization etc.) on dictionary visualization • WordNet built a rich network of relationships, which fundamentally departed from the paper dictionary tradition, and has been used in many computational projects • However very little has been done in the way of interfaces that make these relationships visible and intelligible to users. • Graphical representations seem particularly important given our target users.

32. MRD Interfaces: WordNet

33. Graph-based visualization • There is a little previous work on graphical representations of dictionaries • For instance, the visual-thesaurus by plumbdesign derived from WordNet • But it is also a good demonstration of how chaotic and confusing graphical interfaces can become.

34. Perils of visualization

35. Graph-based visualization (Jansz 1998; Jansz, Manning and Indurkhya 1999) • Classic graph layout problem • Adapts work by Eades et al. (1998) and Huang et al. (1998) on visualization and navigation of WWW document linkages • Uses the spring algorithm. Big advantage is that it is an iterative updating algorithm, and so gives an easy interactivity: • it wiggles and people can play with it. • Clarity and simplicity of graph: Software maintains a set of focus nodes to prevent overcrowding

36. Educational advantages • Alphabetical order is important, but • A web of words offers other effective opportunities for learning • A student can opportunistically explore words that are related in various ways • Important semantic relationships can be understood

37. Kirrkirr network display

38. Kirrkirr network display

39. Formatted dictionary entries • Are produced automatically from the XML by using XSL (a style language) • XSL allows easy modeling of some user preferences. • Most trivially, one can leave out information such as part of speech, or detailed definitions • This is useful as many users find information overload quite confusing and demotivating • Can produce bilingual or monolingual dictionary • Opportunities for various output styles, and formats such as RTF or TeX for printing.

40. Formatted dictionary entries

41. Rich typology of link types • The semantically rich types of linkages present in a dictionary (synonym, antonym, hyponym, subheadword, variant, coverbs, …) solves one of the major problems of the web: we have many link types with a clear semantic interpretation • Use consistent color-coded text and edges to show these link types • Gives a richer browsing experience • Can tell where you are going before clicking

42. Browsing • Work (at PARC and elsewhere: Pirolli et al. 1996) has stressed role for browsing as well as searching in information access • It provides a context for learning • We provide browsing in several ways: • conventional hypertext • but with rich semantically-interpreted links • their color-coding matches network edges • network-based display of words • browsing through semantic domains

43. Semantic Domains • Alphabetical order is one indexing strategy, but there are many others • Most requested is ability to find things by semantic domains: e.g., food, manufactured items. • Essentially the nouns structure of WordNet, or the classical KR ISA hierarchy • We can exploit the domain info in the dictionary

44. Semantic Domains (Katrina Avila)

45. Other components • Multimedia (currently pictures and audio) • Can hear pronunciations / see objects • I’m keen to put in videos of Warlpiri sign language … • Advanced search page • search various fields, regular expressions, etc. • Notes: one can annotate dictionary entries (to correct or personalize)

46. Simple features • Show the alphabet • The list on the left gives concreteness, and tangibility • people can start with one of those words • One can just type a few letters and then look at the list – traditional benefit of paper dictionary • English lookup can be helpful when Warlpiri spelling fails

47. Fuzzy spelling • We expected problems with spelling • Literacy skills based mainly in English, which doesn’t transfer well • Different sounds in Warlpiri • Software employs “fuzzy spelling” which allows generous matching ignoring many distinctions • done on the fly with FSMs, rather than using the SOUNDEX strategy • Still not enough: e.g., one kid wrote “wanapy” when wanting warnapari ‘dingo’, the end part of which knocked us out.

48. Adding more links: Terminology sets • Related words often aren’t in same domain • Rather, words associated with a topic • E.g., a dance has an associated set of words: clearing the ground, decorating with ochres, leaves, and feathers, singing, dancing • A concept useful to native speakers and learners • Such cultural information is hard to learn, and not normally in dictionaries or thesauri • Question: can terminology sets be derived automatically from appropriate corpora?

49. Terminology sets • Approach: terminology will be determined as “medium range” collocations • Corpus: collection of Warlpiri stories, letters, books, fieldwork notes, etc. I have slightly over 1/4 million words of online Warlpiri • This is a large proportion of Warlpiri available in textual form: the difference between fieldwork corpora and StatNLP corpora.

50. Building and assessing • I stemmed words (to maximize “fuel”) – Warlpiri also has clitics that attach to words • Using a Kay/Röscheisen-style “approximate” morphological analysis [ + vowel harmony] • Collocational bonds were assessed using Dunning (1993)’s method of loglikelihood ratios