Introduction to ComputationalLinguisticsPD Dr. Frank Richter
(all slides provided by Prof. Dr. Erhard W. Hinrichs)
Seminar fur Sprachwissenschaft
Eberhard-Karls-Universitat Tubingen
Germany
NLP Intro – WS 2005/6 – p.1
Definition of CL (1a)
Computational linguistics is the scientific studyof language from a computational perspective.
Computational linguists are interested inproviding computational models of various kindsof linguistic phenomena. These models may be"knowledge-based" ("hand-crafted") or "data-driven" ("statistical" or "empirical").
NLP Intro – WS 2005/6 – p.2
Definition of CL (1b)
Work in computational linguistics is in some casesmotivated from a scientific perspective in that oneis trying to provide a computational explanation fora particular linguistic or psycholinguistic phenomenon;and in other cases the motivation may be more purelytechnological in that one wants to provide a workingcomponent of a speech or natural language system.
http://www.aclweb.org/archive/what.html
NLP Intro – WS 2005/6 – p.3
Definition of CL (2)
Computational linguistics is the application of linguistictheories and computational techniques to problems ofnatural language processing.
http://www.ba.umist.ac.uk/public/
departments/registrars/academicoffice/
uga/lang.htm
NLP Intro – WS 2005/6 – p.4
Definition of CL (3)
Computational linguistics is the science of languagewith particular attention given to the processingcomplexity constraints dictated by the human cognitivearchitecture. Like most sciences, computationallinguistics also has engineering applications.
http://www.cs.tcd.ie/courses/csll/
CSLLcourse.html
NLP Intro – WS 2005/6 – p.5
Definition of CL (4)
Computational linguistics is the study of computersystems for understanding and generating naturallanguage.
Ralph Grishman, Computational
Linguistics: An Introduction,
Cambridge University Press 1986.
NLP Intro – WS 2005/6 – p.6
Two Approaches in CL
Rule-Based SystemsExplicit encoding of linguistic knowledgeUsually consisting of a set of hand-crafted,grammatical rulesEasy to test and debugRequire considerable human effortOften based on limited inspection of the data with anemphasis on prototypical examplesOften fail to reach sufficient domain coverageOften lack sufficient robustness when input data arenoisy
NLP Intro – WS 2005/6 – p.7
Two Approaches in CL
Data-Driven SystemsImplicit encoding of linguistic knowledgeOften using statistical methods or machine learningmethodsRequire less human effortAre data-driven and require large-scale data sourcesAchieve coverage directly proportional to therichness of the data sourceAre more adaptive to noisy data
NLP Intro – WS 2005/6 – p.8
Central Goal of the Field
build psychologically adequate models of humanlanguage processing capabilities on the basis ofknowledge about the way in which humans acquire,store, and process language.
build functionally correct models of human languageprocessing capabilities on the basis of knowledge aboutthe world and about language elicited from people andstored in the system.
NLP Intro – WS 2005/6 – p.9
Application Areas
machine translation
speech recognition
speech synthesis
man-machine interfaces
NLP Intro – WS 2005/6 – p.10
Application Areas
intelligent word processing: spelling correction,grammar correction
document managementfind relevant documents in collectionsestablish authorship of documentscatch plagiarismextract information from documentsclassify documentssummarize documentssummarize document collections
NLP Intro – WS 2005/6 – p.11
A bit of Philosophy of Science
Theory:A set of statements that determine the format andsemantics of descriptions of phenomena in the purviewof the theory
Methodology:An effective theory comes with an explicit methodologyfor acquiring these descriptions
Application:A theory associated with a methodology can be appliedto tasks for which the methodology is appropriate.
NLP Intro – WS 2005/6 – p.12
Scientific Strategies
Method Oriented Approach:devise or import a tool, a procedure or a formalism,apply it to a task and develop it further. Then(optionally) see whether it works for additional tasks
Task oriented Approach:select a task; devise or import a method or severalmethods for its solution; integrate the methods asrequired to improve performance.
NLP Intro – WS 2005/6 – p.13
Machine Translation
What makes Machine Translation an important applicationarea to study:
historically first application area, and for at least adecade the only application area, of computationallinguistics
requires all steps relevant to linguistic analysis of inputsentences and linguistic generation of output sentences
hence, machine translation is scientifically one of themost challenging and most comprehensive tasks incomputational linguistics
NLP Intro – WS 2005/6 – p.14
Machine Translation
What makes Machine Translation an important applicationarea to study:
historically first application area, and for at least adecade the only application area, of computationallinguistics
requires all steps relevant to linguistic analysis of inputsentences and linguistic generation of output sentences
hence, machine translation is scientifically one of themost challenging and most comprehensive tasks incomputational linguistics
NLP Intro – WS 2005/6 – p.14
Machine Translation
What makes Machine Translation an important applicationarea to study:
historically first application area, and for at least adecade the only application area, of computationallinguistics
requires all steps relevant to linguistic analysis of inputsentences and linguistic generation of output sentences
hence, machine translation is scientifically one of themost challenging and most comprehensive tasks incomputational linguistics
NLP Intro – WS 2005/6 – p.14
The Purposes of Translation
Information Acquisition:e.g. Gather information on scientific articles ornewspapers written in a foreign language.
Information Dissemination:e.g. Translation of technical manuals, legal texts,weather reports, etc.
Literary Translation:e.g. Translation of novels, poems, etc.
NLP Intro – WS 2005/6 – p.15
The Purposes of Translation
Information Acquisition:e.g. Gather information on scientific articles ornewspapers written in a foreign language.
Information Dissemination:e.g. Translation of technical manuals, legal texts,weather reports, etc.
Literary Translation:e.g. Translation of novels, poems, etc.
NLP Intro – WS 2005/6 – p.15
The Purposes of Translation
Information Acquisition:e.g. Gather information on scientific articles ornewspapers written in a foreign language.
Information Dissemination:e.g. Translation of technical manuals, legal texts,weather reports, etc.
Literary Translation:e.g. Translation of novels, poems, etc.
NLP Intro – WS 2005/6 – p.15
Relating Translation Purposes to MT
Information Acquisition:involves translation from a foreign to a nativelanguage
typically used by non-linguists with little or nolinguistic competence in the source languagepre-processing of the input not feasible due to lack oflinguistic competence by the user in the sourcelanguagemay require special-purpose lexicalow-quality translation is tolerable
NLP Intro – WS 2005/6 – p.16
Relating Translation Purposes to MT
Information Acquisition:involves translation from a foreign to a nativelanguagetypically used by non-linguists with little or nolinguistic competence in the source language
pre-processing of the input not feasible due to lack oflinguistic competence by the user in the sourcelanguagemay require special-purpose lexicalow-quality translation is tolerable
NLP Intro – WS 2005/6 – p.16
Relating Translation Purposes to MT
Information Acquisition:involves translation from a foreign to a nativelanguagetypically used by non-linguists with little or nolinguistic competence in the source languagepre-processing of the input not feasible due to lack oflinguistic competence by the user in the sourcelanguage
may require special-purpose lexicalow-quality translation is tolerable
NLP Intro – WS 2005/6 – p.16
Relating Translation Purposes to MT
Information Acquisition:involves translation from a foreign to a nativelanguagetypically used by non-linguists with little or nolinguistic competence in the source languagepre-processing of the input not feasible due to lack oflinguistic competence by the user in the sourcelanguagemay require special-purpose lexica
low-quality translation is tolerable
NLP Intro – WS 2005/6 – p.16
Relating Translation Purposes to MT
Information Acquisition:involves translation from a foreign to a nativelanguagetypically used by non-linguists with little or nolinguistic competence in the source languagepre-processing of the input not feasible due to lack oflinguistic competence by the user in the sourcelanguagemay require special-purpose lexicalow-quality translation is tolerable
NLP Intro – WS 2005/6 – p.16
Relating Translation Purposes to MT(2)
Information Dissemination:involves translation from a native to a foreignlanguage
pre- and post-processing of the input feasible due tolinguistic competence by the translator in the sourcelanguagemay involve sublanguage with restricted vocabulary;e.g. translation of weather reportsoften involves special terminologies stored in aterminology database; e.g. for translation oftechnical manualspurely human translation for such tasks can betime-consuming, inconsistent, or tedious.
NLP Intro – WS 2005/6 – p.17
Relating Translation Purposes to MT(2)
Information Dissemination:involves translation from a native to a foreignlanguagepre- and post-processing of the input feasible due tolinguistic competence by the translator in the sourcelanguage
may involve sublanguage with restricted vocabulary;e.g. translation of weather reportsoften involves special terminologies stored in aterminology database; e.g. for translation oftechnical manualspurely human translation for such tasks can betime-consuming, inconsistent, or tedious.
NLP Intro – WS 2005/6 – p.17
Relating Translation Purposes to MT(2)
Information Dissemination:involves translation from a native to a foreignlanguagepre- and post-processing of the input feasible due tolinguistic competence by the translator in the sourcelanguagemay involve sublanguage with restricted vocabulary;e.g. translation of weather reports
often involves special terminologies stored in aterminology database; e.g. for translation oftechnical manualspurely human translation for such tasks can betime-consuming, inconsistent, or tedious.
NLP Intro – WS 2005/6 – p.17
Relating Translation Purposes to MT(2)
Information Dissemination:involves translation from a native to a foreignlanguagepre- and post-processing of the input feasible due tolinguistic competence by the translator in the sourcelanguagemay involve sublanguage with restricted vocabulary;e.g. translation of weather reportsoften involves special terminologies stored in aterminology database; e.g. for translation oftechnical manuals
purely human translation for such tasks can betime-consuming, inconsistent, or tedious.
NLP Intro – WS 2005/6 – p.17
Relating Translation Purposes to MT(2)
Information Dissemination:involves translation from a native to a foreignlanguagepre- and post-processing of the input feasible due tolinguistic competence by the translator in the sourcelanguagemay involve sublanguage with restricted vocabulary;e.g. translation of weather reportsoften involves special terminologies stored in aterminology database; e.g. for translation oftechnical manualspurely human translation for such tasks can betime-consuming, inconsistent, or tedious.
NLP Intro – WS 2005/6 – p.17
Relating Translation Purposes to MT(3)
Literary Translationrequires stylistic elegance, often involvesmetaphorical and metonymic language
abundance of highly-trained human translatorstask rarely performed by machine translation
NLP Intro – WS 2005/6 – p.18
Relating Translation Purposes to MT(3)
Literary Translationrequires stylistic elegance, often involvesmetaphorical and metonymic languageabundance of highly-trained human translators
task rarely performed by machine translation
NLP Intro – WS 2005/6 – p.18
Relating Translation Purposes to MT(3)
Literary Translationrequires stylistic elegance, often involvesmetaphorical and metonymic languageabundance of highly-trained human translatorstask rarely performed by machine translation
NLP Intro – WS 2005/6 – p.18