12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Domain-specific Web Corpora and their Applications Gregor Erbach Saarland University Project COLLATE (funding: BMBF 01 IN A01 B)

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Outline Part I: Web Corpora Part II: Applications of Web Corpora Part III: LT-World Web Corpus Part IV: Research in COLLATE

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Part I: Web Corpora 1.Formal Properties of the Web 2.Web Corpus 3.Document and Hyperlink Database 4.TREC web track

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Formal Properties of the Web Hypertext/Hypermedia Directed graph with cycles Edges = hyperlinks Nodes = documents ??? Nodes often have internal tree structure (HTML, XML)

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Web Corpus A web corpus consists of a database of documents a database of hyperlinks

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Document Database Information for each document: –URL/URN –Full Text (possibly with linguistic annotation such as POS, named entities, phrases) –Full Text Index –Metadata Author, Language, Date, MIME type … (Dublin Core) Category, Abstract, Keywords, Type of Page …

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Fields of Hyperlink Database source anchor URL source anchor position on web page (percentage) source anchor position in document structure (HTML element path) source anchor type (text or image) source anchor text and context target anchor URL target anchor position on web page target anchor MIME type

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Derived Properties of Hyperlinks Same document? Same server? Same 2 nd /3 rd level domain? Ascending of descending in directory structure Source is within a list of links Navigation link (up, previous, next …)

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University TREC web track Construction of a web corpus (WT10g) according to the following criteria: –Broadly representative of web data in general –Many inter-server links –Contains all available pages from a set of servers –Contains an interesting set of meta-data –Contains few binary, non-English or duplicate documents –Size: 10 GB P. Bailey, N. Craswell and D. Hawking. Engineering a multi-purpose test collection for Web retrieval experiments. IP&M, to appear.

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Part II: Applications of Web Corpora 1.Web Mining 2.Information Retrieval 3.Clustering and Categorisation 4.Summarisation 5.Discovery of Relations 6.Terminology Extraction 7.Information Extraction 8.Ontology Learning

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Useful Methods Machine Learning and Data Mining Natural Language Processing Information Retrieval Ontologies and Semantic Web Bibliometrics (citation analysis ~ link analysis)

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Web Mining Web Content Mining –Discovery of terminology, acronyms, concepts Web Structure Mining –Discovery of relations, communities … Web Usage Mining –Discovery of navigation patterns

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Information Retrieval Usage of hyperlinks for determining popularity of web pages Hub and authority pages Widely used: Google PageRank Mixed results in TREC web track Jon M. Kleinberg (1997) Authoritative Sources in a Hyperlinked Environment. Journal of the ACM Sergey Brin, Lawrence Page (1998) The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Clustering Standard clustering algorithms form clusters by iteratively grouping documents/clusters, according to a distance measure Content-based methods measure distance by counting terms/concepts (often TF/IDF) Connectivity-based distance measures make use of hyperlinks

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Categorisation Categorisation algorithms determine the membership of a document in a pre-defined thematic category Content-based categorisation methods measure distance from a representative of the category Connectivity-based distance measures are based on the assumption that certain types of hyperlinks lead to documents of the same category

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Summarisation / Keyword Extraction Source anchor text has been used to generate short summaries of target web pages.

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Discovery of Relations Hyperlink structure reflects relations between web resources (e.g. between personal homepage, project page, organisation page) Relations can be discovered by content-based methods and by connectivity-based methods

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Terminology Extraction Content-based: extraction of domain terminology by statistical analysis (TF/IDF …) and/or phrasal chunking Applicability of connectivity-based methods?

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Information Extraction Automatic extraction of meta-data Extraction of named entities for concept-based indexing Extraction of templates/relations for relation-based indexing, and question answering

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Ontology Learning Extraction of candidates by frequency of occurrence in similar contexts Usage of textual clues (“such as”, “sogar” …) Applicability of connectivity-based methods? Definition and acronym mining

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Part III: LT-World Web Corpus 1.Content of LT World 2.Ontology 3.Hyperlinking within LT World 4.Construction of the corpus

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University LT World: Idea and Context The virtual information center is a comprehensive WWW- based information and knowledge service for the entire area of language technology. LT World is a “virtual” center in the sense that most information will physically remain with their creators or with other service providers. The virtual information center has been online since October 2001 under the name „LT World“ for „Language Technology World“ (

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Virtual Information Center - LT World Information and Knowledge –Technical and Scientific Information Players and Teams –Persons, Projects, Organisations Resources and Results –Research Systems, Commercial Products Communication and Events –News, Conferences

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University LT World Ontology Publi cations ProductsProjects People Layer 2: Specific Ontologies Corporaetc. Layer 1: Dublin Core Layer 3: Ontology for CL & LT

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University LT World Ontology Dimensions –Linguality (monolingual, multilingual, cross-language) –Application –Computational/mathematical methods –Linguistic Models / Theories –Level of linguistic description/processing –Technologies –Language(s) Ontology is modelled in RDF with Protégé 2000

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University LT World: Coverage 99 topic nodes 300 NLP tools and products 1800 people 850 organisations 500 projects

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Data Acquisition Process Manual collection, categorization and annotation of URLs by students and staff Sources: conference proceedings and journals, lists of links on the web, Self-registration and correction of data by users of the service Technical/scientific information in topic nodes has been provided by domain experts

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University LT World: Topic Nodes Topic nodes are the main information unit of the Area “Knowledge and Information”. They are organized in a shallow slightly multidimensional hierarchy following the chapter plan of the second edition of the Language Technology Survey. Example of the shallow hierarchy: Information Extraction Named Entity Recognition Terminology Extraction Relation Extraction Answer Extraction

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Information for each Topic Name Acronyms aka‘s, Term Translations Short Definition Overview Article (from HLT Survey) Topic Websites R&D Prototypes/Products Projects People Literature

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Hyperlinking between Sections

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Corpus Construction Start from URLs in LT-World collection Expand document set by recursively following outgoing hyperlinks using a webspider (e.g., GNU wget) Expand document set by following incoming hyperlinks (“link” query to search engine) Expand document set by search engine queries with domain terminology Construct document database and link database (Filter out irrelevant documents) Publish Corpus

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Part IV: Research Directions Categorisation / Information Extraction Discovery of Relations for Hyperlinking Other

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Categorisation and Information Extraction Research objectives –find method for categorising documents according to LT-World ontology –find method for extraction of meta-information Compare and combine content-based and connectivity-based methods If successful, it will contribute to semi-automatic extension of the coverage of LT-World

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Discovery of Relations Objective: develop method for finding pairs of related documents, e.g. personal page – organisation page. Content-based and connectivity-based methods are applicable If successful, it will enable a significant improvement of LT-World (resource discovery, resource annotation)

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Other Objective: compare and combine content-based and connectivity-based clustering methods Applications: 1.Information Retrieval 2.Clustering 3.Summarisation 4.Terminology Extraction 5.Ontology Learning

12 July 2002Colloquim on Applications of Natural Langauge Corpora, Saarland University Conclusion Main research interest: comparison and combination of content-based and connectivity- based methods Main application impact: going from a set of “seed” web pages to a domain-specific information system