Language Technologies (1)

1. ACAI 05 ADVANCED COURSE ON KNOWLEDGE DISCOVERY Language Technologies (1) Diana Maynard University of Sheffield, UK

2. Text mining and the Semantic Web

3. What is Text Mining? • Text mining is about knowledge discovery from large collections of unstructured text. • It’s not the same as data mining, which is more about discovering patterns in structured data stored in databases. • Similar techniques are sometimes used, however text mining has many additional constraints caused by the unstructured nature of the text and the use of natural language. • Information extraction (IE) is a major component of text mining. • IE is about extracting facts and structured information from unstructured text.

4. Challenge of the Semantic Web • The Semantic Web requires machine processable, repurposable data to complement hypertext • Once metadata is attached to documents, they become much more useful and more easily processable, e.g. for categorising, finding relevant information, and monitoring • Such metadata can be divided into two types of information: explicit and implicit.

5. Metadata extraction • Explicit metadata extraction involves information describing the document, such as that contained in the header information of HTML documents (titles, abstracts, authors, creation date, etc.) • Implicit metadata extraction involves semantic information deduced from the material itself, i.e. endogenous information such as names of entities and relations contained in the text. This essentially involves Information Extraction techniques, often with the help of an ontology.

6. Motivation • Implicit or semantic metadata extraction and annotation is the glue that ties ontologies into document spaces • Metadata is the link between knowledge and its management • Manual metadata production cost is too high • State-of-the-art in automatic annotation needs extending to target ontologies and scale to industrial document stores and the web

7. Information Extraction (IE)

8. IE is not IR IR pulls documents from large text collections (usually the Web) in response to specific keywords or queries. You analyse the documents. IE pulls facts and structured information from the content of large text collections. You analyse the facts.

9. IE for Document Access • With traditional query engines, getting the facts can be hard and slow • Where has the Queen visited in the last year? • Which places on the East Coast of the US have had cases of West Nile Virus? • Which search terms would you use to get this kind of information? • How can you specify you want someone’s home page? • IE returns information in a structured way • IR returns documents containing the relevant information somewhere (if you’re lucky)

10. IE as an alternative to IR • IE returns knowledge at a much deeper level than traditional IR • Constructing a database through IE and linking it back to the documents can provide a valuable alternative search tool. • Even if results are not always accurate, they can be valuable if linked back to the original text

11. Some example applications • HaSIE • KIM • Threat Trackers

12. HaSIE • Application developed by University of Sheffield, which aims to find out how companies report about health and safety information • Answers questions such as: “How many members of staff died or had accidents in the last year?” “Is there anyone responsible for health and safety?” “What measures have been put in place to improve health and safety in the workplace?”

13. HASIE • Identification of such information is too time-consuming and arduous to be done manually • IR systems can’t cope with this because they return whole documents, which could be hundreds of pages • System identifies relevant sections of each document, pulls out sentences about health and safety issues, and populates a database with relevant information

14. HASIE

15. KIM • KIM is a software platform developed by Ontotext for semantic annotation of text. • KIM performs automatic ontology population and semantic annotation for Semantic Web and KM applications • Indexing and retrieval (an IE-enhanced search technology) • Query and exploration of formal knowledge

16. KIM Ontotext’s KIM query and results

17. Threat tracker • Application developed by Alias-I which finds and relates information in documents • Intended for use by Information Analysts who use unstructured news feeds and standing collections as sources • Used by DARPA for tracking possible information about terrorists etc. • Identification of entities, aliases, relations etc. enables you to build up chains of related people and things

18. Threat tracker

19. Named Entity Recognition: the cornerstone of IE • Identification of proper names in texts, and their classification into a set of predefined categories of interest • Persons • Organisations (companies, government organisations, committees, etc) • Locations (cities, countries, rivers, etc) • Date and time expressions • Various other types as appropriate

20. Why is NE important? • NE provides a foundation from which to build more complex IE systems • Relations between NEs can provide tracking, ontological information and scenario building • Tracking (co-reference) “Dr Head, John, he” • Ontologies “Manchester, CT” • Scenario “Dr Head became the new director of Shiny Rockets Corp”

21. Knowledge Engineering rule based developed by experienced language engineers make use of human intuition require only small amount of training data development can be very time consuming some changes may be hard to accommodate Learning Systems use statistics or other machine learning developers do not need LE expertise require large amounts of annotated training data some changes may require re-annotation of the entire training corpus Two kinds of approaches

22. Typical NE pipeline • Pre-processing (tokenisation, sentence splitting, morphological analysis, POS tagging) • Entity finding (gazeteer lookup, NE grammars) • Coreference (alias finding, orthographic coreference etc.) • Export to database / XML

23. An example: GATE • GATE (Generalised Architecture for Text Engineering) is a framework for language processing GATE also includes: • plugins for language processing, e.g. parsers, machine learning tools, stemmers, IR tools, IE components for various languages... • tools for visualising and manipulating ontologies • ontology-based information extraction tools • evaluation and benchmarking tools

24. GATE Users • American National Corpus project • Perseus Digital Library project, Tufts University, US • Longman Pearson publishing, UK • Merck KgAa, Germany • Canon Europe, UK • Knight Ridder, US • BBN (leading HLT research lab), US • SMEs: Melandra, SG-MediaStyle, ... • a large number of other UK, US and EU Universities • UK and EUprojects inc. SEKT, PrestoSpace, KnowledgeWeb, MyGrid, CLEF, Dot.Kom, AMITIES, CubReporter, …

25. Past Projects using GATE • MUMIS: conceptual indexing: automatic semantic indices for sports video • MUSE: multi-genre multilingual IE • HSL: IE in domain of health and safety • Old Bailey: IE on 17th century court reports • Multiflora: plant taxonomy text analysis for biodiversity research in e-science • EMILLE: creation of S. Asian language corpus • ACE/ TIDES: IE competitions and collaborations in English, Chinese, Arabic, Hindi • h-TechSight: ontology-based IE and text mining

26. Current projects using GATE • ETCSL: language tools for Sumerian digital library • SEKT: Semantic Knowledge Technologies • PrestoSpace: Preservation of audiovisual data • KnowledgeWeb: Semantic Web network of excellence • SWAN: Large-scale semantic annotation • LIRICS: Linguistic infrastructure for Interoperable Resources and Systems

27. Architectural principles ofGATE • Non-prescriptive, theory neutral (strength and weakness) • Re-use, interoperation, not reimplementation (e.g. diverse XML support, integration of Protégé, Jena, Weka...) • (Almost) everything is a component, and component sets are user-extendable • (Almost) all operations are available both from API and GUI

28. GATE

29. Information Extraction for the Semantic Web • Traditional IE is based on a flat structure, e.g. recognising Person, Location, Organisation, Date, Time etc. • For the Semantic Web, we need information in a hierarchical structure • Idea is that we attach semantic metadata to the documents, pointing to concepts in an ontology • Information can be exported as an ontology annotated with instances, or as text annotated with links to the ontology

30. Richer NE Tagging • Attachment of instances in the text to concepts in the domain ontology • Disambiguation of instances, e.g. Cambridge, MA vs Cambridge, UK

31. Another example: Magpie • Developed by the Open University • Plugin for standard web browser • Automatically associates an ontology-based semantic layer to web resources, allowing relevant services to be linked • Provides means for a structured and informed exploration of the web resources • e.g. looking at a list of publications, we can find information about an author such as projects they work on, other people they work with, etc.

32. MAGPIE in action

33. MAGPIE in action

34. Evaluation

35. Evaluation metrics and tools • Evaluation metrics mathematically define how to measure the system’s performance against human-annotated gold standard • Scoring program implements the metric and provides performance measures • for each document and over the entire corpus • for each type of NE • may also evaluate changes over time • A gold standard reference set also needs to be provided – this may be time-consuming to produce • Visualisation tools show the results graphically and enable easy comparison

36. Methods of evaluation • Traditional IE is evaluated in terms of Precision and Recall • Precision - how accurate were the answers the system produced? correct answers/answers produced • Recall - how good was the system at finding everything it should have found? correct answers/total possible correct answers • Usually a tradeoff between precision and recall, so a weighted average of the two (F-measure) is generally also used.

37. GATE AnnotationDiff Tool

38. Metrics for Richer IE • Precision and Recall are not sufficient for ontology-based IE, because the distinction between right and wrong is less obvious • Recognising a Person as a Location is clearly wrong, but recognising a Research Assistant as a Lecturer is not so wrong • Similarity metrics need to be integrated so that items closer together in the hierarchy are given a higher score, if wrong • Also possible is a cost-based approach, where different weights can be given to each concept in the hierarchy, and to different types of error, and combined to form a single score

39. Learning Accuracy • LA [Hahn98] originally defined to measure how well a concept had been added in the right level of the ontology • LA measures “the degree to which the system correctly predicts the concept class which subsumes the target concept to be learned”. • Used by Cimiano et al [2003] to measure how well the instance has been added in the right place in the ontology.

40. Learning Accuracy Metric SP = the shortest length from root to the key concept FP = shortest length from root to the predicted concept. If the predicted concept is correct, then FP = 0, i.e. FP is only considered in the case that the answer given by the system is wrong. CP = shortest length from root to the MSCA (the lowest concept common to SP and FP paths) DP = shortest length from MSCA to predicted concept If predicted concept is correct, i.e. if FP =0, then LA = CP / SP = 1 If predicted concept is incorrect, LA = CP / FP + DP

41. Problems with LA • LA doesn’t consider the height of the Key concept, which means that however far away the Key is from the MSCA, the score is the same • It also means that similarity is not bidirectional, which is intuitively wrong • We propose an alternative to LA, known as BDM (Balanced Distance Metric) which takes this into account

42. BDM • MSCA – most specific concept common to Key and Response • CP – distance from root to MSCA • DPR – distance from MSCA to Response concept • DPK – distance from MSCA to Key concept Each one is normalised wrt average length of chain in which Key and Response occur This makes the penalty in terms of node traversal relative to the semantic density of the concepts in question

43. BDM - normalisations n1: average length of the set of chains containing the key or the response concept, computed from the root concept. n2: average length of all the chains containing the key concept, computed from the root concept. n3: average length of all the chains containing the response concept, computed from the root concept.

44. BDM – the metric • BDM is calculated for all correct and partially correct responses CP = distance from root to MSCA DPK = distance from MSCA to Key DPR = distance from MSCA to Response

45. BDM: observations • BDM considers the relative specificity of the taxonomic positions of the key and response • It does not distinguish between the directionality of this relative specificity, however. • For instance, the key can be a specific concept (e.g. 'car') and the response can be a general concept (e.g. 'relation'), or vice versa. • Either way, the score will be the same.

46. Augmented Precision and Recall BDM is integrated with traditional Precision and Recall in the following way:

47. Creating a gold standard corpus • OntoNews corpus: 292 news articles from 3 news agencies (Guardian, Financial Times, Independent) • 3 topics: international politics, UK politics and business. • covers August – October 2001 • Corpus annotated manually wrt KIMO ontology

48. KIMO: a reference ontology • KIMO is earlier version of the Proton ontology, created by Ontotext in scope of KIM platform • http://proton.semanticweb.org • Contains around 250 classes and 100 relations • Domain-independent and modular (comprises top ontology and more specific lower ontology)

49. Annotating OntoNews • Annotation set covers range of levels and types of semantic annotation • Decomposable into subsets that constitute 3 types of ontologies: • Named entities • Top ontology (20 high level concepts) • Common nouns • Coverage is significantly greater than previous initiatives, e.g. MUC, ACE