Computational Linguistics

1. Computational Linguistics Ling 200 Spring 2006

2. Speech and language processing • Computational Linguistics • use of computers to facilitate linguistic research • Natural Language Processing • computer-natural language interface applications

3. Combines disciplines • Linguistics • e.g. grammar engineering • Electrical Engineering • e.g. speech recognition • Computer science • e.g. machine translation • Psychology • e.g. cognitive modeling

4. 2 Minute question (part 1) List the specific language related skills HAL exhibits. In other words, list the different abilities the computer (HAL) must have to display human-like language?

6. Today’s goals • Convey: • some areas of research • some of the difficulties involved • some development strategies • Provide examples of particular technologies as illustration

7. Computerized natural language • speech recognition • language understanding • language generation • speech synthesis

8. Other areas of interest • searching • understanding search request • finding relevant documents • ordering by degree of relevance • information extraction • retrieving information from documents • data mining • discovering patterns and relationships in data

9. ...and still more topics • machine translation • http://babelfish.altavista.com • http://www.google.com/translate • summarization • grammar checking • spell checking

10. Commonly used tools • formal rule systems • computational search algorithms • formal logic • probability theory • machine learning techniques

11. Speech Recognition Demo Software Used: iListen from MacSpeech

12. What is Speech Recognition? • Definition: Speech recognition turns acoustic input into strings of phonemes and then finds the best matching word in a database. • Can be built for open domain use, theoretically recognizing all possible strings of words • e.g. dictation systems • Can also be built for a particular domain, recognizing small, finite sets of utterances • e.g. automated call-centers.

13. Speech RecognitionAcoustic Model • First, the continuous speech signal is broken up into short segments. • Segments are analyzed into features, which you can think of as quantitative versions of the phonetic features you learned in class. • By comparing segments against internally stored phonological model, well matched phonemes are proposed for each segment • End up with a list of most likely phoneme sequences.

14. Speech RecognitionLanguage Model • Sequences of phonemes are verified by comparing with a database of words and their likelihoods (in real time), and only actual words and phrases are accepted • [rɛkənajspič]  • [rɛkənajspič] • ‘recognizespeech’ • [rɛkənajspiš]  • [rɛkənajspiš] • ??‘recognizespeesh’ *Fast speech: [z] -> [s] / _[s]

15. Problems Acoustic Model • Recognizing different voice qualities as the same basic sounds. • You can think of this as choosing the correct phoneme. • Phonemes sound different (allophones), depending on their environments. • word position: /p/ --> [ph] / #_ • assimilation: /z/ --> [s] / _C [-voice] • deletion: [s] --> ø / _[s] • “Three cats sit.” • Speech signal is continuous and full of non-speech noise.

16. ProblemsAmbiguity • Same or very similar sequence of phonemes can correspond to multiple words or phrases • Homophones • Words • [dir] ‘deer’ ‘dear’ • Phrases (remember there is no pause to separate word boundaries) • [rɛkənajspič] ‘recognize speech’ • [rɛkənajspič] ‘wreck a nice beach’

17. Potential FixLanguage Model • Weight word/phrase interpretations (statistical language modeling) • Lexical: Consider how often a word actually occurs. • [dir] ‘deer’ (50) ‘dear’ (215) • Choose most frequent, in this case ‘dear’ • Condition on context: Consider how often a word occurs within a particular context. • I just shot a [dir]. (shot, a, dear) 1 (shot, a, deer) 10 • In this case, ‘deer’ occurs more frequently in this environment, so we choose ‘deer’ as our interpretation.

18. DemoTraining Data Matters • Word and context frequencies are not just pulled from thin air. • Frequencies are calculated (training) • From some collection of text (a corpus). • Speech recognizers often train on a user’s emails and documents, to better match the user’s lexical choice and phrase patterns. • This training data helps decipher homophonous strings (strings that are acoustically ambiguous).

19. Demo 2Training Data Matters • I will attempt to utter the following phrase and iListen should transcribe my speech. • It’s hard to… • [rɛkənajspič]‘recognize speech’ • [rɛkənajspič]‘wreck a nice beach’

20. Demo 3 Linguist • What if software is trained for a Computational Linguist? • Trained on 3 Wikipedia articles about various topics in Computational Linguistics • Which interpretation should we expect, based on words and phrases likely to be present in computational linguistics documents? • Results:Is hard to recognize speech New set the state • but is so bad and found a 544 is no sound better, even so it is etc is not really that bad so at and his exist listening to 89, Nancy of

21. Demo 4 Beach Bum • What if software is trained for a Beach Bum? • Trained on 3 Wikipedia articles on beach topics. • Which interpretation should we expect, based on frequent words and phrases likely to be found in beach-related documents? • Results:It’s hard to wreck nice beach and

22. Language understanding • morphology • syntax • semantics • pragmatics • discourse

23. "I made her duck.” • I cooked waterfowl for her • I cooked waterfowl belonging to her • I created the (plaster?) duck she owns • I caused her to quickly lower her head or body • I waved my magic wand and turned her into undifferentiated waterfowl

24. Language generation “I'm sorry, Dave, I'm afraid I can't do that” • pragmatics: • politeness • indirect speech • morphology: • contractions • discourse: • reference (“that”)

25. Who/what is ELIZA?

26. Dialogue systems - issues • HAL has complete understanding - How close are we to this? • Eliza had no semantic understanding and only minimal syntactic knowledge • dialogue systems: effective in limited domains like travel

27. Dialogue systems: demo [David] • Chatbot website: • http://daden.co.uk/chatbots/

28. 2 minute question (part 2) • Do you think that HAL quality computer communication is a reasonable expectation? • Why or why not?