Introduction to Computational Linguistics

Jay Munson(special thanks to Misty

Azara)

May 30, 2003

Today’s Goals

I. Introduction to Computational Linguistics (CL) through the discussion of 7 CL core areas.

II. Identify Common CL applications III. Identify the importance of

theoretical linguistics in CL

What is Computational Linguistics?

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

What is Computational Linguistics (CL)?

CL is interdisciplinary Linguistics Computer Science Mathematics Electrical Engineering Speech and Hearing Science

Seven Core Areas of CL

1. Machine Translation 2. Speech Recognition 3. Text-to-Speech 4. Natural Language Generation 5. Human-Computer Dialogs 6. Information Retrieval 7. Computational Modeling

1.0 Machine Translation (MT)

Using computers to automate some or all of translating from

one language to another

1.1 MT (cont.)

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

1.2 MT (cont.)

Linguistic knowledge is extremely useful in this area of CL

MT benefits from knowledge of language typology and language-specific linguistic information

Programs are typically “trained” using pre-translated documents/texts.

1.3 MT Example KANT Knowledge-based Machine Translation The KANT project, Knowledge-based, Accurate Translation

for technical documentation, was founded in 1989 for the research and development of large-scale, practical translation systems for technical documentation. KANT uses a controlled vocabulary and grammar for each source language, and explicit yet focused semantic models for each technical domain to achieve very high accuracy in translation. Designed for multilingual document production, KANT has been applied to the domains of electric power utility management and heavy equipment technical documentation.

http://www.lti.cs.cmu.edu/Research/cmt-projects.html

2.0 Speech Recognition (SR)

Taking spoken language as input and outputting the

corresponding text

2.1 SR - 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

Contexts are “contained” to improve accuracy

2.2 Why use SR?

Allow for hands-free human-computer interaction

Assists in automated telephony

3.0 Text-to-Speech (TTS)

Taking text as input and outputting the corresponding

spoken language

3.1 Three types of TTS 1. Articulatory- models the

physiological characteristics of the vocal tract

2. Concatenative- uses pre-recorded segments to construct the utterance(s) ScanSoft: Jennifer and Susana

http://www.scansoft.com/realspeak/demo/ Speechify: British Female

http://www.speechworks.com/demos/speechify.cfm

3.2 Three types of TTS (cont.)

3. Parametric/Formant- models the formant transitions of speech ETI-Eloquence: Reed

http://www.speechworks.com/demos/eti.cfm

3.3 Why is TTS so difficult?

Spelling through, rough, though, thought

Homonyms PERmit (n) vs. perMIT (v)

Prosody (dependent on context) 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!!”

3.4 Why use TTS?

Allows for text to be read automatically

Extremely useful for the visually impaired and the hearing impaired.

For a review of the history of TTS until 1987 with sound files, goto:

http://www.ece.ogi.edu/~macon/ECE580/klatt/

4.0 Natural Language Generation (NLG)

Constructing linguistic outputs from non-linguistic inputs; the NLG goal is to produce natural language

from internal data/structure.

4.1 Natural language generation (cont) 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

4.2 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

4.3 NLG (cont)

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).

4.4 NLG - Structures

Discourse structure- many NLG systems have to deal with multi-sentence discourses, which must have a coherent structure

4.5 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.…

5.0 Human-Computer Dialogs

Uses a mix of SR, TTS, and pre-recorded prompts to

achieve some goal

5.1 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

5.2 Human-Computer Dialogs

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

5.3 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?…

5.4 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)

5.5 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

6.0 Information Retrieval

Storage, analysis, and retrieval of text documents

6.1 Information Retrieval (IR)

Most current IR systems are based on some interpretation of “compositional semantics” (e.g. the meaning of the whole is based the meaning of its parts and their combination).

IR is the core of web-based searching, i.e. Google, Altavista, etc.

6.2 IR - Architecture

User inputs a word or string of words

System processes the words and retrieves documents corresponding to the request

6.3 “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!

6.4 Linguistic Knowledge in IR

Semantics Compositional Lexical

Syntax (depending on the model used)

7.0 Computational Modeling

Computational approaches to problem solving, modeling,

and development of theories

7.1 How can we use computational modeling?

Develop working models of language evolution

Model speech perception, production, and processing

Almost any theoretical model can have a computational counterpart

7.2 Why Use Computational Modeling?

Forces explicitness – no black boxes or behind the scenes “magic”

Allows us to test our formal theories given a large amount of data

Allows for enhancements in technology and benefits to society through the implementaions of models.

Conclusions

CL applications utilize linguistic knowledge from all of the major subfields of theoretical linguistics (e.g. theory is necessary!)

Computational modeling can aid/test linguists’ theories of language processing and structure

Conclusions - Review of 7 core areas in CL

1. Machine Translation 2. Speech Recognition 3. Text-to-Speech 4. Natural Language Generation 5. Human-Computer Dialogs 6. Information Retrieval 7. Computational Modeling

Conclusions – Review of Today’s Goals

I. Introduction to Computational Linguistics (CL) through the discussion of 7 CL core areas.

II. Identify Common CL applications III. Identify the importance of

theoretical linguistics in CL

El fin.