The Semantics and Pragmatics of Natural Language

The Semantics and Pragmatics

of Natural Language

Daniela GÎFU

http://profs.info.uaic.ro/~daniela.gifu/

“ALEXANDRU IOAN CUZA” UNIVERSITATY OF IAŞI

FACULTY OF COMPUTER SCIENCE

Course 1

SPNL OVERVIEW

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https://profs.info.uaic.ro/~daniela.gifu/

Who am I?

“Alexandru Ioan Cuza” University of Iași

T H E H A L L O F T H E L O S T S T E P S

Faculty of Computer Science

BE AMONG THE FIRST…..

Romanian Academy

What is this course about?

➢ Meaning and Natural Language Processing (NLP)

➢ Computational Semantics

➢ Computational Pragmatics

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Familiarization

with relevant terminology

• Semantics

• Pragmatics

• Natural language

• Computational Linguistics

• Natural Language Processing

…9

Simulation of human (natural)

intelligence by machines

Interdisplinary field ~

Scientific study of

language from a

computational

perspective

A discipline that spans

theory and practice to

understand

computer systems and

networks at a deep level.10

Computational Linguistics (CL)

vs.

Natural Language Processing (CLP)

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CL = gives theoretical background (computational

theories on language), linguistics models.

NLP = applied CL, including:

- natural language technology (NLT)

- human language technology (HLT)

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Researches

Engineering techniques have to be underpinned by scientific

understanding…

Good performances in some

tasks when large amount of data

(with annotation) are available

Spoken language

- speech processing (from speech to text to syntax and

semantics to speech) - https://speechlogger.appspot.com/ro/

Ex: mobile

Written language – my area of interest

Language in correlation with other modalities

(multimodality)

- speech

- intonation

- image

Ex: GPS (Global Positioning System)13

Natural Language Technology

Document segmentation and interpretation

– cleaning (elimination of dots, enhancing contrast,

etc.)

– separation of text from image, curved lines...

– recognizing printed, semi-uncial characters, etc.

• Optical Character Recognition (OCR)

~ 100% accuracy in scanning printed Latin script

based material

Challenge in OCR

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Written Language Technologies

Students?

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OCR Handwriting – Why?

= presents some unique particularities

= many varieties of cursive writing

see: https://pdf.iskysoft.com/ocr-pdf/handwriting-ocr.html

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OCR Handwriting very challenging

= the interpretation of physician handwriting (Rasmussen,

L.V. et al., 2012; Broda. B. & Piasecki, M., 2007)

= analysis of old handwritten documents (useful for linguists,

musicians, historians, etc.)

Document Image

Analysis

PR = a sub-topic of machine learning

(description or classification (recognition) of

measurements.

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Differences between CL Approaches

•Analysis and understanding of written language

– sub-syntactic processing

• lexical units

• sentence splitting

• clause borders

• part of speech and morphological information

• lemmas

• entity names

• groups (nominal, verbal, prepositional, etc.)

and lexical attractions (collocations)

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Written Language Technologies

• Language analysis and understanding

– semantic and discourse processing

• semantic disambiguation → word senses

• semantic roles labeling → NLTK

• rhetorical structure of discourse and dialogue →

RST (Rhetorical Structure Theory)

• anaphora resolution → StandfordCoreNLP

• text summarization → Machine Learning

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Written Language Technologies

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the study of mathematical structures and methods that are

of importance to linguistics.

→ Phonetics → Phonology → Morphology →

Syntax and → Semantics → and…

Sociolinguistics → Language Acquisition.

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Mathematical Linguistics

Mathematical Linguistics before Computational Linguistics….

ML ⇔ CL?

= art of solving problems that need to analyze

(or generate) natural language text.

Find that metrics for a good solution to the

engineering problem…

NLP

Google Translate – Don’t blame!!!!

Romanian = Luceafărul de dimineață

English = The morning gentleman (bad answer)

= Morning star (good answer)

Why????

explains how human translators do their job...

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Let’s try!

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NLP – a subdomain of

Artificial Intelligence & Linguistics

Thematic Areas

- Linguistics - mathematical linguistics - computational

linguistics

- Formal Language

- Linguistic and Language Processing

- The grammatical structure of utterances: the sentence,

constituents, phrase, classifications and structural rules,

syntactic processing ...

- Parser or Syntax Analyzer

- Semantics & Pragmatics

= an area of Artificial Intelligence (AI) devoted to

creating computers that use NL as input and/or

output.

NLP

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AI-hard problem

= machine reading

comprehension

= produces language

as output on the basis

of data input

= developing computational methods/models of human

linguistics behavior.

CL

▪ INFORMATION RETRIEVAL

▪ INFORMATION EXTRACTION

▪ MACHINE TRANSLATION

▪ QUESTION – ANSWERING

▪ SUMMARIZATION

▪ MACHINE READABLE DICTIONARIES

▪ SPELLING & GRAMMAR CHECKERS

…

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Let’s describe and exemplify

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A discipline concerned with understanding written and spoken

language from a computational perspective.

- detecting synonymy (Grigonytė et al., 2010);

- developing WordNet (including Romanian - Gala et Mititelu,

2013), (Iftene and Balahur, 2007)...;

- WSD (Yang, H. et al. 2010), (Lefever et Hoste, 2010), (Tufiș,

2002)...;

- semantic annotation (Garcia et al., 2012)...;

- reconstructing a diachronic morphology (Cristea et al.,

2007/2012)

- diachronic text classification (Mihalcea and Năstase, 2012;

Popescu and Strapparava, 2015), etc.

- epoch detection (Gifu, 2015/2016/2017)...;

CL – Applications

Tools developed

by students…

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Linguistic & Language Processing

1. Linguistics

- Science of language. Includes:

✓ Sounds (phonology)

✓ Word formation (morphology)

✓ Sentence structure (syntax)

✓ Meaning (semantics) and understanding

(pragmatics)…

2. Levels of linguistic analysis

- Higher level → Speech Recognition (SR)

- Lower levels → Natural Language Processing (NLP)

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Levels of Linguistic Analysis

NLP

Letters - strings

Morphemes

Words

Phrases & sentences

Meaning out of context

Meaning in context

Phonemes

Acoustic signal

Speech

Recognition

Phonetics – production and perception of speech

Phonology – Sound patterns of language

Lexicon – Dictionary of words in a language

Morphology – Word formation and structure

Syntax – Sentence structure

Semantics – Intended meaning

Pragmatics – Understanding from external info

NLP Pipeline

Course purpose

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MAIN CONCEPTS

1. Natural Language

- used by human beings for communication...

- sign, system, symbols, rule-set (or grammar)

2. Semantics

- literal meaning determined from a word, phrase,

sentence.

3. Pragmatics

- contextual meaning {situation, speaker, etc.}

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Natural or ordinary language

• A system of speech symbols → (form criterion)

Types:

a) speech (spoken language)

b) signing (written language) - the representation of a spoken or

gestural language.

• The most important means of human communication →

(function criterion)

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Natural Language…• Multiplicity of languages

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Formal Language_I

1. Symbol

- a character, an abstract entity that has no meaning by

itself

Ex: lettters, digits and special characters

2. Alphabet

- finite set of symbols

- often denoted by Σ

Ex:

B = {0, 1} says B is an alphabet of two symbols, 0 and 1

C = {a, b, c} – C an alphabet of 3 symbols, a, b and c

* More about formal language:

http://www.its.caltech.edu/~matilde/FormalLanguageTheory.pdf

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Formal Language_II

3. String or word

- a finite sequence of symbols from an alphabet

Ex: 01110 and 111 are strings from the alphabet B above

aaabccc and b are strings from the C above

4. Sentence

- a string of words.

Ex: I saw the gentleman with the hat.

String = a b c d e b f

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Formal language_III

Define possible relations of parts of a string to each other?

A.

[I] saw the gentleman [with the binocular] = [a] b c d [e b f]

B.

I saw [the gentleman with the binocular] = a b [c d e b f ]

We can represent structures with trees…

I saw the gentleman with the binocular. I saw the gentleman with the binocular.

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Formal Language_IV

5. Language

- a set of strings of symbols from an alphabet.

6. Natural Language or ordinary language

- open-ended = built on 3 different knowledge components: the

sound of words - phonology; the meaning of words -

semantics; the grammatical rules according to which words are

put together - syntax.

7. Formal language

- a set L of sequences/strings over some finite alphabet Σ

- described using formal grammars (a set of rules for strings,

specified to it).

- many application (e.g., Prognosis wearable system)

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Formal Language_VContext-Free Grammars (CFG) - a finite set of grammar rules https://www.tutorialspoint.com/automata_theory/context_free_grammar_introduction.htm

= a quadruple (N, T, P, S) , where:

N = a finite set of non-terminal symbols (character or variable).

Note! Each n ∈ N = type of phrase/clause in the sentence.

T = a finite set of terminals (an alphabet, defined by the grammar) disjoint of N: N ∩ T = NULL.

P = a finite set of (rewrite) rules or productions of the grammar, from N to

P: N → (N ∪ T)*

Note! The left-hand side of the production rule P does have any right context or left

context. * = Kleene star operation = unary operation on sets of strings or sets of symbols or

characters → a set N is written as N* (used for regular expressions).

Ex: {"a", "b", "c"}* = {ε, "a", "b", "c", "aa", "ab",

"ac", "ba", "bb", "bc", "ca", "cb", "cc", "aaa", "aab",

...} - {ε} (the language consisting only of the empty string)

S = start symbol/start symbol, used to represent the whole sentence.

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Main Concepts - IICONCLUSIONS

Computational semantics and pragmatics:

➢ automatic construction of semantic representations for NL

expressions (in context).

➢ automatic inferences over the representations.

Major Issues:

➢Ambiguity of various levels:

lexical, syntactic, semantic, pragmatic

➢ Interface between LF from linguistic form and context of use

(essential for modelling anaphora).

Tools used include:

➢ Information: syntax, world knowledge, lexical semantics,

corpora…

➢ Inference: logic (model checkers and theorem proving), machine

learning, statistics…

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Semester Homework:

1. Each student has to present a paper about

his/her SEMEVAL task that guide final project

- https://aclweb.org/anthology/

between 2018-2021

EMNLP (Empirical Methods on Natural Language

Processing)

ACL (Association of Computational Linguistics)

EACL (European Association of Computational

Linguistics)

COLING (International Conference on

Computational Linguistics) …

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Final project: SEMEVAL 2022

Groups structured by 2-3 students:

- 1-2 humanists & 1 computer scientists prepare a paper

at the SEMEVAL-2022 based to their research

supervised constantly -

https://semeval.github.io/SemEval2022/tasks

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Projects steps – next time

1. Form a team...

2. Choose a task

3. Define the teamwork

4. Establish the modular structure

5. Edit the paper – a possible structure

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5. Edit the paper – making and outline

* Choosing a Title

* Abstract (executive summary) & Keywords

* Introduction (the new approach; background

information; research problem/question; theoretical

framework)

* SOTA (citation tracking; content alert services;

evaluating sources; primary sources; secondary sources…)

* Methodology (qualitative methods; quantitative

methods)

* Results

* Discussion

* Conclusions and future work

* References

Thank you!

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