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 KANT 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.

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 Speechify: British Female

3.2 Three types of TTS (cont.) 3. Parametric/Formant- models the formant transitions of speech ETI-Eloquence: Reed

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: latt/ latt/

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 file 1. Choose save from the file menu 2. Choose the appropriate folder 3. Type the file name 4. 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, 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.