1 / 21

HLT, Data Sparsity and Semantic Tagging Louise Guthrie (University of Sheffield)

HLT, Data Sparsity and Semantic Tagging Louise Guthrie (University of Sheffield) Roberto Basili (University of Tor Vergata, Rome) Hamish Cunningham (University of Sheffield). Outline. A ubiquitous problem: data sparsity The approach: coarse-grained semantic tagging

marsha
Download Presentation

HLT, Data Sparsity and Semantic Tagging Louise Guthrie (University of Sheffield)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. HLT, Data Sparsity and Semantic Tagging • Louise Guthrie (University of Sheffield) • Roberto Basili (University of Tor Vergata, Rome) • Hamish Cunningham (University of Sheffield) 1(21)

  2. Outline • A ubiquitous problem: data sparsity • The approach: • coarse-grained semantic tagging • learning by combining multiple evidence • The evaluation: intrinsic and extrinsic measures • The expected outcomes: architectures, tools, development support 2(21)

  3. Applications PresentWe’ve seen growing interest in a range of HLT tasks: e.g. IE, MT Trends • Fully portable IE, unsupervised learning • Content Extraction vs. IE 3(21)

  4. Data Sparsity • Language Processing depends on a model of the features important to an application. • MT - Trigrams and frequencies • Extraction - Word patterns • New texts always seem to have lots of phenomena we haven’t seen before 4(21)

  5. Different kinds of patterns Person was appointed as postof company Company named person to post • Almost all extraction systems tried to find patterns of mixed words and entities. • People, Locations, Organizations, dates, times, currencies 5(21)

  6. Can we do more? Astronauts aboard the space shuttle Endeavor were forced to dodge a derelict Air Force satellite Friday Humans aboard space_vehicle dodge satellitetimeref. 6(21)

  7. Could we know these are the same? The IRA bombed a family owned shop in Belfast yesterday. FMLN set off a series of explosions in central Bogota today. ORGANIZATION ATTACKED LOCATION DATE 7(21)

  8. Machine translation • Ambiguity of words often means that a word can translate several ways. • Would knowing the semantic class of a word, help us to know the translation? 8(21)

  9. Sometimes . . . • Crane the bird vs crane the machine • Bat the animal vs bat for cricket and baseball • Seal on a letter vs the animal 9(21)

  10. SO .. P(translation(crane) = grulla | animal) > P(translation(crane) = grulla) P(translation(crane) = grua | machine) > P(translation(crane) = grua) Can we show the overall effect lowers entropy? 10(21)

  11. Language Modeling – Data Sparseness again .. • We need to estimate Pr (w3 | w1 w2) • If we have never seen w1w2 w3 before • Can we instead develop a model and estimate Pr (w3 | C1 C2) or Pr (C3 | C1 C2) 11(21)

  12. A Semantic Tagging technology. How? • We will exploit similarity with NE tagging, ... • Development of pattern matching rules as incremental wrapper induction • ... with semantic (sense) disambiguation • Use as much evidence as possible • Exploit existing resources like MRD or LKBs • ... and with machine learning tasks • Generalize from positive examples in training data 12(21)

  13. Multiple Sources of Evidence • Lexical information (priming effects) • Distributional information from general and training texts • Syntactic features • SVO patterns or Adjectival modifiers • Semantic features • Structural information in LKBs • (LKB-based) similarity measures 13(21)

  14. Machine Learning for ST • Similarity estimation • among contexts (texts overlaps, …) • among lexical items wrt MRD/LKBs • We will experiment • Decision tree learning (e.g. C4.5) • Support Vector Machines (e.g. SVM light) • Memory-based Learning (TiMBL) • Bayesian learning 14(21)

  15. What’s New? • Granularity • Semantic categories are coarser than word senses (cfr. homograph level in MRD) • Integration of existing ML methods • Pattern induction is combined with probabilistic description of word semantic classes • Co-training • Annotated data are used to drive the sampling of further evidence from unannotated material (active learning) 15(21)

  16. How we know what we’ve done: measurement, the corpus • Hand-annotated corpus • from the BNC, 100-million word balanced corpus • 1 million words annotated • a little under ½ million categorised noun phrases • Extrinsic evaluationPerplexity of lexical choice in Machine Translation • Intrinsic evaluationStandard measures or precision, recall, false positives • (baseline: tag with most common category = 33%) 16(21)

  17. Ambiguity levels in the training data NPs by semantic categories: 0 104824 23.1% 1 119228 26.3% 2 96852 21.4% 3 44385 9.8% 4 35671 7.9% 5 15499 3.4% 6 13555 3.0% 7 7635 1.7% 8 6000 1.3% 9 2191 0.5% 10 3920 0.9% 11 1028 0.2% 12 606 0.1% 13 183 0.0% 14 450 0.1% 15 919 0.2% 17 414 0.1% Total NPs (interim) 453360 17(21)

  18. Maximising project outputs:software infrastructure for HLT • Three outputs from the project: • 1. A new resource • Automatical annotation of the whole corpus • Experimental evidence re. 1.- how accurate the final results are- how accurate the various methods employed are • Component tools for doing 1., based on GATE(a General Architecture for Text Engineering) 18(21)

  19. What is GATE? • An architectureA macro-level organisational picture for LE software systems. • A frameworkFor programmers, GATE is an object-oriented class library that implements the architecture. • A development environmentFor language engineers, computational linguists et al, GATE is a graphical development environment bundled with a set of tools for doing e.g. Information Extraction. • Some free components... ...and wrappers for other people's components • Tools for: evaluation; visualise/edit; persistence; IR; IE; dialogue; ontologies; etc. • Free software (LGPL). Download at http://gate.ac.uk/download/ 19(21)

  20. Where did GATE come from? • A number of researchers realised in the early- mid-1990s (e.g. in TIPSTER): • Increasing trend towards multi-site collaborative projects • Role of engineering in scalable, reusable, and portable HLT solutions • Support for large data, in multiple media, languages, formats, and locations • Lower the cost of creation of new language processing components • Promote quantitative evaluation metrics via tools and a level playing field • History: • 1996 – 2002: GATE version 1, proof of concept • March 2002: version 2, rewritten in Java, component based, LGPL, more users • Fall 2003: new development cycle 20(21)

  21. Role of GATE in the project • Productivity- reuse some baseline components for simple tasks- development environment support for implementors (MATLAB for HLT?)- reduce integration overhead (standard interfaces between components)- system takes care of persistency, visualisation, multilingual edit, ... • Quantification- tool support for metrics generation - visualisation of key/response differences- regression test tool for nightly progress verification • Repeatability- open source supported, maintained, documented software- cross-platform (Linux, Windows, Solaris, others)- easy install and proven useability (thousands of people, hundreds of sites)- mobile code if you write in Java; web services otherwise 21(21)

More Related