1. Some extra stuff Semantic change that results in an antonym of the original word:Semantic change that results in an antonym of the original word: awful: original meaning: 'awe-inspiring, filling (someone) with deep awe', as in the awful majesty of the Creator; new meaning: 'breath- takingly bad; so bad that it fills (a person) with awe and amazement'awful: original meaning: 'awe-inspiring, filling (someone) with deep awe', as in the awful majesty of the Creator; new meaning: 'breath- takingly bad; so bad that it fills (a person) with awe and amazement' Compare it to awesomeCompare it to awesome Similar pair: terrible vs. terrificSimilar pair: terrible vs. terrific

2. Some extra stuff cool vs. hot: they could be both positive (e.g. That’s really cool; hot buys) but not always (e.g. hot check)cool vs. hot: they could be both positive (e.g. That’s really cool; hot buys) but not always (e.g. hot check) Intermediate ones like lukewarm do not have positive or negative meanings.Intermediate ones like lukewarm do not have positive or negative meanings.

3. Computational Linguistics Ling 400

4. What is Linguistics good for? Language teachingLanguage teaching Medical: speech therapy for aphasic patientsMedical: speech therapy for aphasic patients Computer-related applicationsComputer-related applications –Computational linguistics (also called natural language processing, language engineering): computer simulation of human language processes

5. Some online materials Some relevant information including a lecture by Prof. Emily Bender is available from the following web site.Some relevant information including a lecture by Prof. Emily Bender is available from the following web site. http://depts.washington.edu/llc/olr/lingui stics/index.phphttp://depts.washington.edu/llc/olr/lingui stics/index.php

6. Computational Linguistics encoding/production: speech synthesis, word processing help, production side of an expert system, generation of sentences in the target language in machine translation.encoding/production: speech synthesis, word processing help, production side of an expert system, generation of sentences in the target language in machine translation. decoding/understanding: speech recognition, parsing, disambiguation via a network of semantic relations.decoding/understanding: speech recognition, parsing, disambiguation via a network of semantic relations.

7. Language Production thinking: cannot be simulatedthinking: cannot be simulated speech/writing: computer simulation of speech sounds is possible to some extent. Computer can help this process with a grammar checker, an input system and a word breaker (in a language like Japanese). But these tasks do not simulate what people actually do when they talk.speech/writing: computer simulation of speech sounds is possible to some extent. Computer can help this process with a grammar checker, an input system and a word breaker (in a language like Japanese). But these tasks do not simulate what people actually do when they talk.

8. Language Production (2) Though not part of the natural production process, turning speech into written text has some practical applications.Though not part of the natural production process, turning speech into written text has some practical applications. This is very useful because speaking is usually quicker than writing. It would be like having a personal secretary.This is very useful because speaking is usually quicker than writing. It would be like having a personal secretary. This is also useful for someone who cannot write because of disability or injury.This is also useful for someone who cannot write because of disability or injury.

9. Language Understanding speech recognition: difficult but possible if the domain is restricted (e.g. speaker and/or expected input types) Why is this difficult?speech recognition: difficult but possible if the domain is restricted (e.g. speaker and/or expected input types) Why is this difficult? syntactic analysis: “parsing” (syntactic analysis by computer) is possible but needs semantic/pragmatic information for disambiguating instances of structural ambiguity.syntactic analysis: “parsing” (syntactic analysis by computer) is possible but needs semantic/pragmatic information for disambiguating instances of structural ambiguity. Interpretation (truth conditions): unclear as to how to simulate this; usually done via semantic representations (in some machine translation systems).Interpretation (truth conditions): unclear as to how to simulate this; usually done via semantic representations (in some machine translation systems).

10. Ambiguity Resolution The astronomer saw a comet with a telescope.The astronomer saw a comet with a telescope. The astronomer saw a comet with a brilliant tail.The astronomer saw a comet with a brilliant tail. How do we solve its ambiguity? One needs to know some information about seeing and comets.

11. Semantic Network

12. Concrete Applications corpus linguisticscorpus linguistics machine translationmachine translation text retrievaltext retrieval text summarizationtext summarization word processing help (discussed above)word processing help (discussed above) expert systemsexpert systems speech recognition/synthesis (touched upon above)speech recognition/synthesis (touched upon above) toys, gamestoys, games automatic telephone interpretation systemautomatic telephone interpretation system ultimately … artificial intelligence, roboticsultimately … artificial intelligence, robotics

13. Corpus Linguistics This is a generic name for various computer applications that make use of large language databases (called corpora)This is a generic name for various computer applications that make use of large language databases (called corpora) Having access to a large database enabled us to process linguistic data in a statistical way, rather than in an analytical way.Having access to a large database enabled us to process linguistic data in a statistical way, rather than in an analytical way. This conflict of two opposing views (statistical vs. analytical) is very apparent in machine translation.This conflict of two opposing views (statistical vs. analytical) is very apparent in machine translation.

14. Machine Translation (1) text-to-text translation (great need for translation at UN, EC (European Community)text-to-text translation (great need for translation at UN, EC (European Community) Works best when two languages in question are similar in structureWorks best when two languages in question are similar in structure Usually, pre-editing and/or post-editing by a human translator is required — machine-assisted translation.Usually, pre-editing and/or post-editing by a human translator is required — machine-assisted translation.

15. Machine Translation (2) Traditionally, MT required parsing, possibly some semantic analysis, then mapping to a syntactic tree of the sentence in the target language.Traditionally, MT required parsing, possibly some semantic analysis, then mapping to a syntactic tree of the sentence in the target language. An alternative is appeal to statistical means of mapping a surface string in the source language to a surface string in the target language.An alternative is appeal to statistical means of mapping a surface string in the source language to a surface string in the target language.http://www.excite.co.jp/world/english/web/

16. Machine Translation (3) Difficulty with word-for-word translationDifficulty with word-for-word translation Watasi-ni-wa futari kodomo-ga imasu. I -at-top two child-nom exist Literally, ‘As for me as a location, two children exist’ Actually, its meaning is ‘I have two children.’

17. Computational Semantics The study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions.The study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions. This could play an important role in such application areas as machine translation when two typologically distinct languages are involved (e.g. English and Japanese).This could play an important role in such application areas as machine translation when two typologically distinct languages are involved (e.g. English and Japanese).

18. Text Retrieval key word  text/book key word: morphology 1.Principles of Polymer Morphology 2.Image Analysis and Mathematical Morphology 3.Drainage Basin Morphology 4.French Morphology We need morphological, syntactic, and semantic information to find the right text/book. Further applications: search engines, etc.

19. Text Summarization We need to be able to select the right information from the electronic documents available (esp. on the web). Automatic text summarization is a technique that can help people to quickly grasp the concepts presented in a document by creating an abstract or summary of the original text.

20. Semantic Web Some people (e.g. Evergreen U) are trying to classify contents of web pages so that they are meaningful to computers. But this is not an easy task since the categories must presumably be pre-selected by people. The semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. http://www.w3.org/2001/sw/The semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. http://www.w3.org/2001/sw/ http://www.w3.org/2001/sw/

21. Speech Recognition/Synthesis actually being used on personal computers (on a limited basis), automated telephone answering system, etc.actually being used on personal computers (on a limited basis), automated telephone answering system, etc. Application of acoustic phonetics, phonologyApplication of acoustic phonetics, phonology

22. AI and robotics Furby Aibo