Marginalia

Jacob Eisenstein introduces computational methods for understanding, generating, and manipulating language.

The book establishes machine-learning tools before applying them to text analysis.

Sequences, trees, and graphs provide structured representations of linguistic information.

Accounts of meaning range from formal logic to neural word embeddings.

Applications include information extraction, machine translation, and text generation.

Series: AI Books · Book deep dive

Introduction to Natural Language Processing — Jacob Eisenstein

Publication source

Explore: AI & Computer Science · Language & Linguistics

Related episodes: Speech and Language Processing (Daniel Jurafsky & James H. Martin) — Part 2 · Speech and Language Processing (Daniel Jurafsky & James H. Martin) — Part 1

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Abelson and Sussman examine how programs are built from reusable ideas and abstractions.

Procedures and data abstractions provide ways to organize increasingly complex computations.

The book studies state, evaluation, and the design of interpreters and compilers.

Scheme examples connect programming techniques with models of how computation works.

Its emphasis is on understanding program structure and creating languages for expressing solutions.

Series: AI Books · Book deep dive

Structure and Interpretation of Computer Programs — Harold Abelson and Gerald Jay Sussman

Publication source

Explore: AI & Computer Science

Related episodes: Introduction to the Theory of Computation (Michael Sipser) · The Information: A History, a Theory, a Flood (James Gleick)

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Jurafsky and Martin survey computational approaches to speech and human language.

The book connects linguistic representations with statistical and machine-learning methods.

Its topics range from words and sequences to syntax, meaning, and language understanding.

Applications show how these methods support systems that analyze and generate language.

The text provides a foundation for studying the algorithms and assumptions behind language technology.

Series: AI Books · Book deep dive

Speech and Language Processing — Daniel Jurafsky and James H. Martin

Publication source

Explore: AI & Computer Science · Language & Linguistics

Related episodes: Speech and Language Processing (Daniel Jurafsky & James H. Martin) — Part 1 · Introduction to Natural Language Processing (Jacob Eisenstein)

Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode!

Jurafsky and Martin survey computational approaches to speech and human language.

The book connects linguistic representations with statistical and machine-learning methods.

Its topics range from words and sequences to syntax, meaning, and language understanding.

Applications show how these methods support systems that analyze and generate language.

The text provides a foundation for studying the algorithms and assumptions behind language technology.

Series: AI Books · Book deep dive

Speech and Language Processing — Daniel Jurafsky and James H. Martin

Publication source

Explore: AI & Computer Science · Language & Linguistics

Related episodes: Speech and Language Processing (Daniel Jurafsky & James H. Martin) — Part 2 · Introduction to Natural Language Processing (Jacob Eisenstein)

Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode!

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Chomsky examines grammars as formal systems that generate the sentences of a language.

Restrictions on production rules define different classes of grammars and languages.

The analysis compares their expressive power and the structures their derivations assign.

These distinctions connect linguistic descriptions with automata and the theory of computation.

The work asks which formal properties matter for representing natural-language syntax.

Series: Language & Linguistics · AI Books · Book chapter

Formal Properties of Grammars — Noam Chomsky

Publication source

Explore: Language & Linguistics · AI & Computer Science

Related episodes: Syntactic Theory: A Formal Introduction (Ivan Sag, Thomas Wasow & Emily Bender) · Noam Chomsky and Major Debates in Linguistics (The Cambridge Companion to Chomsky)

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Michael Sipser introduces the mathematical foundations of computation.

Automata and formal languages explain what different computational models can recognize.

Computability theory asks which problems can be solved by algorithms at all.

Complexity theory studies how the required time and memory grow with a problem’s size.

Proofs and examples connect these questions to the capabilities and limits of computers.

Series: AI Books · Book deep dive

Introduction to the Theory of Computation — Michael Sipser

Publication source

Explore: AI & Computer Science

Related episodes: Structure and Interpretation of Computer Programs (Harold Abelson & Gerald Jay Sussman) · The Information: A History, a Theory, a Flood (James Gleick)

Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode!

R. Murray Schafer examines the changing sonic environments in which people live.

He follows the transformation from natural and rural sounds to industrial and urban noise.

Listening becomes a way to study how communities experience their surroundings.

The book distinguishes sounds that enrich an environment from those that overwhelm it.

Schafer develops an approach to acoustic design aimed at healthier and more attentive soundscapes.

Series: Art & Music

The Soundscape: Our Sonic Environment and the Tuning of the World — R. Murray Schafer

Publication source

Explore: Art & Music · Mind & Psychology

Related episodes: On Late Style: Music and Literature Against the Grain (Edward Said) · Music and the Ineffable (Vladimir Jankélévitch)

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