introduction to computational linguistics misty azara
TRANSCRIPT
Introduction to Computational Linguistics
Misty Azara
Agenda
Introduction to Computational Linguistics (CL)
Common CL applications Using CL in theoretical linguistics
(computational modeling)
What is Computational Linguistics?
CL is interdisciplinary Linguistics Computer Science Mathematics Electrical Engineering Psychology Speech and Hearing Science
What is Computational Linguistics?
Computational Linguistics covers many areas
Essentially, CL is any task, model, algorithm, etc. that attempts to place any type of language processing (syntax, phonology, morphology, etc.) in a computational setting
Core Areas of CL Machine Translation Speech Recognition Text-to-Speech Natural Language Generation Human-Computer Dialogs Information Retrieval Computational Modeling…
Machine Translation
Using computers to automate some or all of translating from
one language to another
Three general models or tasks: Tasks for which a rough translation is
adequate Tasks where a human post-editor can
be used to improve the output Tasks limited to a small sublanguage
Machine Translation (cont.)
Linguistic knowledge is extremely useful in this area of CL
MT benefits from knowledge of language typology and language-specific linguistic information
Speech Recognition
Taking spoken language as input and outputting the
corresponding text
Architecture
SR takes the source speech and produces “guesses” as to which words could correspond to the source via some type of acoustic model
The word with the highest probability is selected as the optimal candidate
Why use SR?
Allow for hands-free human-computer interaction
Text-to-Speech
Taking text as input and outputting the corresponding
spoken language
Three types of TTS
Articulatory- models the physiological characteristics of the vocal tract
Concatenative- uses pre-recorded segments to construct the utterance(s)
Three types of TTS (cont.)
Parametric/Formant- models the formant transitions of speech
[baj]
Why is TTS so difficult?
Spelling through, rough
Homonyms PERmit (n) vs. perMIT (v)
Prosody Pitch, duration of segments, phrasing of
segments, intonational tune, emotion“I am so angry at you. I have never been more enraged in my
life!!”
Why use TTS?
Allows for text to be read automatically
Extremely useful for the visually impaired
Natural Language Generation
Constructing linguistic outputs from non-linguistic
inputs
Natural Language Generation Maps meaning to text Nature of the input varies greatly
from one application to another (i.e documenting structure of a computer program)
The job of the NLG system is to extract the necessary information to drive the generation process
NLG systems have to make choices:
Content selection- the system must choose the appropriate content for input, basing its decision on a pre-specified communicative goal
Lexical selection- the system must choose the lexical item most appropriate for expressing a concept
Sentence Structure Aggregation- the system must
apportion the content into phrase, clause, and sentence-sized chunks
Referential expression- the system must determine how to refer to the objects under discussion (not a trivial task)
Discourse structure- many NLG systems have to deal with multi-sentence discourses, which must have a coherent structure
Sample NLG output
To save a file1. Choose save from the file menu2. Choose the appropriate folder3. Type the file name4. Click the save button
The system will save the document.…
Human-Computer Dialogs
Uses a mix of SR, TTS, and pre-recorded prompts to
achieve some goal
Human-Computer Dialogs
Uses speech recognition, or a combination of SR and touch tone as input to the system
The system processes the spoken information and outputs appropriate TTS or pre-recorded prompts
Dialog systems have specific tasks, which limit the domain of conversation
This makes the SR problem much easier, as the potential responses become very constrained
Sample dialog system for banking
…Sys: would you like information for
checking or savings? User: Checking, please.Sys: Your current balance is $2,568.92.
Would you like another transaction?User: Yes, has check #2431 cleared?…
Linguistic knowledge in dialog systems
Discourse structure- ensuring natural flowing discourse interaction
Building appropriate vocabularies/lexicons for the tasks
Ensuring prosodic consistencies (i.e. questions sound like questions and spliced prompts sound continuous)
Why use human-computer systems?
Automate simple tasks- no need for a teller to be on the other end of the line!
Allow access to system information from anywhere, via the telephone
Information Retrieval
Storage, analysis, and retrieval of text documents
Information Retrieval
Most current IR systems are based on some interpretation of compositional semantics
IR is the core of web-based searching, i.e. Google, Altavista, etc.
Information Retrieval Architecture
User inputs a word or string of words
System processes the words and retrieves documents corresponding to the request
“Bag of Words”
The dominant approach to IR systems is to ignore syntactic information and process the meaning of individual words only
Thus, “I see what I eat” and “I eat what I see” would mean exactly the same thing to the system!
Linguistic Knowledge in IR
Semantics Compositional Lexical
Syntax (depending on the model used)
Computational Modeling
Computational approaches to problem solving, modeling,
and development of theories
How can we use computational modeling? Test our theories of language
change~ synchronic or diachronic Develop working models of
language evolution Model speech perception,
production, and processing Almost any theoretical model can
have a computational counterpart
Why Use Computational Modeling?
Forces explicitness – no black boxes or behind the scenes “magic”
Allows for modeling that would otherwise be impossible
Allows for modeling that would otherwise be unethical
Conclusions
CL applications utilize linguistic knowledge from all of the major subfields of theoretical linguistics
Computational modeling can aid linguists’ theories of language processing and structure