I was wondering if Computational Semantics is actually used in any real-world system? (Simple examples here and here). I would like to see how an actual system works.
It seems like there are a bunch of issues with actually using Computational Semantics in any real world system:
It seems just labeling sentences with part-of-speech tags is error prone.
But you also need a reliable parse tree which is error prone and there can be many valid trees for one sentence.
Finding what pronouns are referring to what entities is error prone.
Word disambiguation is also another source of errors and multiple meanings could be valid in the same context.
Any context-free-grammar of English I can find seems to be incomplete.
Finally, after all these sources of error are dodged, we can finally convert the sentence to FOL with Computation Semantics!
Also, I can't seem to figure out how to deal with prepositions in Computation Semantics.
Is this really just an academic exercise or is Computational Semantics actually useful?
There are several better aproaches to natural language than simple lambda calculus and context free grammars, ie. HPSG, Montague Grammar, TAG, ...
Word disambiguation can be handled by Markov chains, for example.
Siri, Google Now, Cortana and IBM Watson are some examples for real world systems.
Google Translate is another application that uses Computational Semantics.
I believe (bu't don't quote me on this) that the technology spun out of the now-defunct Natural Language Theory and Technology group at the Palo Alto Research Center (PARC, formerly Xerox PARC), utilizes the lambda calculus to provide inferences about textual entailments. idk i only worked there a summer (freshman, so was wonderfully igorant of most of the goings-on there).
Anyway, that 'technology' was developed over 30 years and then Powerset bought the right to all of it for $15 million, attempting to disrupt smart search in general. Then Bing's fatass came along, gobbled it up nom nom nom, then continued devouring the entire research group as whole. The principal core investigators now work solely as adjuct profs at Stanford. Sad.
I am French, and am a former Certified Network Security Administrator.
I went back to university 3 years ago to achieve a Bachelor's degree in linguistics, and I am now going to enroll in a Masters Degree in Computer Science applied to Linguistics, with the objective of eventually trying to go through a Doctorate (but I'm not there yet :-) ).
The course will focus on speech recognition, automatic language translation, statistical analysis of texts, speech encoding and decoding, and information abstratction from textual sources.
The professors will let us use any computer language we want to use to code the algorithms and programs we will develop during the curriculum.
I used to develop web apps as a side gig for about 3-4 years and I am proficient in Javascript as I wrote software that used node.js at the server end and the browser at the client. I also have some familiarity with postgresql.
My current style of coding (if we can call that a style) is mainly procedural and I use object prototyping as my main way to create/manage objects in my code. I don't have much experience with object oriented language that use the concept of classes to manage the objects. Therefore I am pretty confident my current coding skills are definitely lacking in regards to what is required for me to write efficient code to deal with that stuff.
So my question is this : what would be the best computer language for me to learn in order to be effective in writing algorithms and data structure suited for the above mentionned linguistic areas?
Thanks in advance for your enlightened answers.
Sat Cit Ananda.
Your question is opinion based, so probably off-topic here.
In France, you have a lot of good courses on Ocaml which is developed at INRIA with several good books (notably, both in French, Developpement d'Applications en Ocaml by Chailloux, Manoury, Pagano; and Programmation de Droite à Gauche & vice versa by Manoury). J.Pitrat also wrote Textes, Ordinateurs et Compréhension; his latest book artificial beings: the conscience of a conscious machines will also interest you.
And learning several programming languages, not only one, is always useful (a single programming language is not enough to do Natural Language Processing; you need to learn several programming languages and several programming paradigms - both functional and object paradigms are useful, and also prolog). You could also start reading the SICP while learning Scheme. Learning more about Lisp-like languages thru Queinnec's book Principe d'implementation de Scheme et Lisp - the updated version of Lisp In Small Pieces will also teach you a big lot.
Java might also be useful (because some NLP libraries are available in Java). CommonLisp, C++2011, Haskell ... too.
Also take time to use and master Linux (and its programming) and free software.
In general, natural language processing requires a lot of computer science (and math).
For production NLP systems, Java seems to be the most common choice. It is a nice and safe language for beginner/intermediate programmers that scales well with codebase size, has a simple grammar and a vast standard library, and it is one of the most commonly used languages where software performance isn't the absolute top priority (or where performance can be scaled horizontally/distributed). I believe for example most of the higher layers of IBM Watson are written in Java. You'll also find it as one of the primary teaching languages in CS courses.
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So I've recently came up with some new possible projects that would have to deal with deriving 'meaning' from text submitted and generated by users.
Natural language processing is the field that deals with these kinds of issues, and after some initial research I found the OpenNLP Hub and university collaborations like the attempto project. And stackoverflow has this.
If anyone could link me to some good resources, from reseach papers and introductionary texts to apis, I'd be happier than a 6 year-old kid opening his christmas presents!
Update
Through one of your recommendations I've found opencyc ('the world's largest and most complete general knowledge base and commonsense reasoning engine'). Even more amazing still, there's a project that is a distilled version of opencyc called UMBEL. It features semantic data in rdf/owl/skos n3 syntax.
I've also stumbled upon antlr, a parser generator for 'constructing recognizers, interpreters, compilers, and translators from grammatical descriptions'.
And there's a question on here by me, that lists tons of free and open data.
Thanks stackoverflow community!
Tough call, NLP is a much wider field than most people think it is. Basically, language can be split up into several categories, which will require you to learn totally different things.
Before I start, let me tell you that I doubt you'll have any notable success (as a professional, at least) without having a degree in some (closely related) field. There is a lot of theory involved, most of it is dry stuff and hard to learn. You'll need a lot of endurance and most of all: time.
If you're interested in the meaning of text, well, that's the Next Big Thing. Semantic search engines are predicted as initiating Web 3.0, but we're far from 'there' yet. Extracting logic from a text is dependant on several steps:
Tokenization, Chunking
Disambiguation on a lexical level (Time flies like an arrow, but fruit flies like a banana.)
Syntactic Parsing
Morphological analysis (tense, aspect, case, number, whatnot)
A small list, off the top of my head. There's more :-), and many more details to each point. For example, when I say "parsing", what is this? There are many different parsing algorithms, and there are just as many parsing formalisms. Among the most powerful are Tree-adjoining grammar and Head-driven phrase structure grammar. But both of them are hardly used in the field (for now). Usually, you'll be dealing with some half-baked generative approach, and will have to conduct morphological analysis yourself.
Going from there to semantics is a big step. A Syntax/Semantics interface is dependant both, on the syntactic and semantic framework employed, and there is no single working solution yet. On the semantic side, there's classic generative semantics, then there is Discourse Representation Theory, dynamic semantics, and many more. Even the logical formalism everything is based on is still not well-defined. Some say one should use first-order logic, but that hardly seems sufficient; then there is intensional logic, as used by Montague, but that seems overly complex, and computationally unfeasible. There also is dynamic logic (Groenendijk and Stokhof have pioneered this stuff. Great stuff!) and very recently, this summer actually, Jeroen Groenendijk presented a new formalism, Inquisitive Semantics, also very interesting.
If you want to get started on a very simple level, read Blackburn and Bos (2005), it's great stuff, and the de-facto introduction to Computational Semantics! I recently extended their system to cover the partition-theory of questions (question answering is a beast!), as proposed by Groenendijk and Stokhof (1982), but unfortunately, the theory has a complexity of O(n²) over the domain of individuals. While doing so, I found B&B's implementation to be a bit, erhm… hackish, at places. Still, it is going to really, really help you dive into computational semantics, and it is still a very impressive showcase of what can be done. Also, they deserve extra cool-points for implementing a grammar that is settled in Pulp Fiction (the movie).
And while I'm at it, pick up Prolog. A lot of research in computational semantics is based on Prolog. Learn Prolog Now! is a good intro. I can also recommend "The Art of Prolog" and Covington's "Prolog Programming in Depth" and "Natural Language Processing for Prolog Programmers", the former of which is available for free online.
Chomsky is totally the wrong source to look to for NLP (and he'd say as much himself, emphatically)--see: "Statistical Methods and Linguistics" by Abney.
Jurafsky and Martin, mentioned above, is a standard reference, but I myself prefer Manning and Schütze. If you're serious about NLP you'll probably want to read both. There are videos of one of Manning's courses available online.
If you get through Prolog until the DCG chapter in Learn Prolog Now! mentioned by Mr. Dimitrov above, you'll have a good beginning at getting some semantics into your system, since Prolog gives you a very simple way of maintaining a database of knowledge and belief, which can be updated through question-answering.
As regards the literature, I have one major recommendation for you: run out and buy Speech and Language Processing by Jurafsky & Martin. It is pretty much the book on NLP (the first chapter is available online); used in a frillion university courses but also very readable for the non-linguist and practically oriented, while at the same time going fairly deep into the linguistics problems. I really cannot recommend it enough. Chapters 17, 18 and 21 seem to be what you're looking for (14, 15 and 18 in the first edition); they show you simple lambda notation which translates pretty well to Prolog DCG's with features.
Oh, btw, on getting the masters in linguistics; if NL semantics is what you're into, I'd rather recommend taking all the AI-related courses you can find (although any courses on "plain" linguistic semantics, logic, logical semantics, DRT, LFG/HPSG/CCG, NL parsing, formal linguistic theory, etc. wouldn't hurt...)
Reading Chomsky's original literature is not really useful; as far as I know there are no current implementations that directly correspond to his theories, all the useful stuff of his is pretty much subsumed by other theories (and anyone who stays near linguists for any matter of time will absorb knowledge of Chomsky by osmosis).
I'd highly recommend playing around with the NLTK and reading the NLTK Book. The NLTK is very powerful and easy to get into.
You could try reading up a bit on phrase structured grammers, which is basically the mathematics behind much language processessing. It's actually not that heavy, being largely based on set and graph theory. I studied it many moons ago as part of a discrete math course, and I guess there are many good references available at this stage.
Edit:Not as much as I expected on google, although this one looks like a good learning source.
One of the early explorers into NLP is Noam Chomsky; he wrote small books on the subject in the 50s through the 70s. You may find that engaging reading.
Cycorp have a short description of how their Cyc knowledge base derives meaning from sentences.
By utilising a massive knowledge base of common facts, the system can determine the most logical parse of a sentence.
A simpler place to begin with the building blocks is the look at the documentation for a package that attempts to do it. I'd recommend the Python [Natural Language Toolkit (NLTK)1, particularly because of their well-written, free book, which is filled with examples. It won't get you all the way to what you want (which is an AI-hard problem), but it will give you a good footing. NLTK has parsers, chunkers, context-free grammars, and more.
This is really hard stuff. I'd start off by getting at least a Masters in Linguistics, and then work towards my PhD in computer science, concentrating on NLP.
The problem is that most of us don't have the understanding of what language is. And without that understanding, it's bloody tough to implement a solution.
Other comments give some readings, which are probably fine if you want to get started playing around with a small subset of the problem, but in order to come up with a really robust solution, then there are no shortcuts. You need the academic background in both disciplines.
A very enjoyable readable introduction is The Language Instinct by Steven Pinker. It goes into the Chomsky stuff and also tells interesting stories from the evolutionary biology angle. Might be worth starting with something like that before diving into Chomsky's papers and related work, if you're new to the subject.
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If you could go back in time and tell yourself to read a specific book at the beginning of your career as a developer, which book would it be?
I expect this list to be varied and to cover a wide range of things.
To search: Use the search box in the upper-right corner. To search the answers of the current question, use inquestion:this. For example:
inquestion:this "Code Complete"
Code Complete (2nd edition) by Steve McConnell
The Pragmatic Programmer
Structure and Interpretation of Computer Programs
The C Programming Language by Kernighan and Ritchie
Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein
Design Patterns by the Gang of Four
Refactoring: Improving the Design of Existing Code
The Mythical Man Month
The Art of Computer Programming by Donald Knuth
Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
Gödel, Escher, Bach by Douglas Hofstadter
Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
Effective C++
More Effective C++
CODE by Charles Petzold
Programming Pearls by Jon Bentley
Working Effectively with Legacy Code by Michael C. Feathers
Peopleware by Demarco and Lister
Coders at Work by Peter Seibel
Surely You're Joking, Mr. Feynman!
Effective Java 2nd edition
Patterns of Enterprise Application Architecture by Martin Fowler
The Little Schemer
The Seasoned Schemer
Why's (Poignant) Guide to Ruby
The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
The Art of Unix Programming
Test-Driven Development: By Example by Kent Beck
Practices of an Agile Developer
Don't Make Me Think
Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
Domain Driven Designs by Eric Evans
The Design of Everyday Things by Donald Norman
Modern C++ Design by Andrei Alexandrescu
Best Software Writing I by Joel Spolsky
The Practice of Programming by Kernighan and Pike
Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
Software Estimation: Demystifying the Black Art by Steve McConnel
The Passionate Programmer (My Job Went To India) by Chad Fowler
Hackers: Heroes of the Computer Revolution
Algorithms + Data Structures = Programs
Writing Solid Code
JavaScript - The Good Parts
Getting Real by 37 Signals
Foundations of Programming by Karl Seguin
Computer Graphics: Principles and Practice in C (2nd Edition)
Thinking in Java by Bruce Eckel
The Elements of Computing Systems
Refactoring to Patterns by Joshua Kerievsky
Modern Operating Systems by Andrew S. Tanenbaum
The Annotated Turing
Things That Make Us Smart by Donald Norman
The Timeless Way of Building by Christopher Alexander
The Deadline: A Novel About Project Management by Tom DeMarco
The C++ Programming Language (3rd edition) by Stroustrup
Patterns of Enterprise Application Architecture
Computer Systems - A Programmer's Perspective
Agile Principles, Patterns, and Practices in C# by Robert C. Martin
Growing Object-Oriented Software, Guided by Tests
Framework Design Guidelines by Brad Abrams
Object Thinking by Dr. David West
Advanced Programming in the UNIX Environment by W. Richard Stevens
Hackers and Painters: Big Ideas from the Computer Age
The Soul of a New Machine by Tracy Kidder
CLR via C# by Jeffrey Richter
The Timeless Way of Building by Christopher Alexander
Design Patterns in C# by Steve Metsker
Alice in Wonderland by Lewis Carol
Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
About Face - The Essentials of Interaction Design
Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky
The Tao of Programming
Computational Beauty of Nature
Writing Solid Code by Steve Maguire
Philip and Alex's Guide to Web Publishing
Object-Oriented Analysis and Design with Applications by Grady Booch
Effective Java by Joshua Bloch
Computability by N. J. Cutland
Masterminds of Programming
The Tao Te Ching
The Productive Programmer
The Art of Deception by Kevin Mitnick
The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
Masters of Doom
Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
How To Solve It by George Polya
The Alchemist by Paulo Coelho
Smalltalk-80: The Language and its Implementation
Writing Secure Code (2nd Edition) by Michael Howard
Introduction to Functional Programming by Philip Wadler and Richard Bird
No Bugs! by David Thielen
Rework by Jason Freid and DHH
JUnit in Action
K&R
#Juan: I know Juan, I know - but there are some things that can only be learned by actually getting down to the task at hand. Speaking in abstract ideals all day simply makes you into an academic. It's in the application of the abstract that we truly grok the reason for their existence. :P
#Keith: Great mention of "The Inmates are Running the Asylum" by Alan Cooper - an eye opener for certain, any developer that has worked with me since I read that book has heard me mention the ideas it espouses. +1
Discrete Mathematics For Computer Scientists http://ecx.images-amazon.com/images/I/51HCJ5R42KL._SL500_BO2,204,203,200_AA219_PIsitb-sticker-dp-arrow,TopRight,-24,-23_SH20_OU02_.jpg
Discrete Mathematics For Computer Scientists by J.K. Truss.
While this doesn't teach you programming, it teaches you fundamental mathematics that every programmer should know. You may remember this stuff from university, but really, doing predicate logic will improve you programming skills, you need to learn Set Theory if you want to program using collections.
There really is a lot of interesting information in here that can get you thinking about problems in different ways. It's handy to have, just to pick up once in a while to learn something new.
Systemantics: How Systems Work and Especially How They Fail. Get it used cheap. But you might not get the humor until you've worked on a few failed projects.
The beauty of the book is the copyright year.
Probably the most profound takeaway "law" presented in the book:
The Fundamental Failure-Mode Theorem (F.F.T.): Complex systems usually operate in failure mode.
The idea being that there are failing parts in any given piece of software that are masked by failures in other parts or by validations in other parts. See a real-world example at the Therac-25 radiation machine, whose software flaws were masked by hardware failsafes. When the hardware failsafes were removed, the software race condition that had gone undetected all those years resulted in the machine killing 3 people.
One of my personal favorites is Hacker's Delight, because it was as much fun to read as it was educational.
I hope the second edition will be released soon!
Concepts, Techniques, and Models of Computer Programming.
alt text http://ecx.images-amazon.com/images/I/51YZ50ZR13L._SL500_AA240_.jpg
Extreme Programming Explained: Embrace Change by Kent Beck. While I don't advocate a hardcore XP-or-the-highway take on software development, I wish I had been introduced to the principles in this book much earlier in my career. Unit testing, refactoring, simplicity, continuous integration, cost/time/quality/scope - these changed the way I looked at development. Before Agile, it was all about the debugger and fear of change requests. After Agile, those demons did not loom as large.
Types and Programming Languages by Benjamin C Pierce for a thorough understanding of the underpinnings of programming languages.
alt text http://ecx.images-amazon.com/images/I/51E0Ojkz8iL._BO2,204,203,200_PIsitb-sticker-arrow-click,TopRight,35,-76_AA300_SH20_OU01_.jpg
Database System Concepts is one of the best books you can read on understanding good database design principles.
The practice of programming. By Brian W. Kernighan, Rob Pike.
The style shown here is excellent - the code just speaks for itself, and the whole book follows the KISS principle. Personally not my languages of choice, but still influential to me.
Programming from the ground up. It's free on the internet. This book taught me AT&T asm. It is very easy to read.
Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig
I started reading it because I wanted to learn Common Lisp. When I was halfway, I realized this was the greatest book about programming I had read so far.
Definitively Software Craftsmanship
alt text http://ecx.images-amazon.com/images/I/5186JKTDVWL._SL500_AA240_.jpg
This book explains a lot of things about software engineering, system development. It's also extremly useful to understand the difference between different kind of product developement: web VS shrinkwrap VS IBM framework. What people had in mind when they conceived waterfall model? Read this and all we'll become clear (hopefully)
#Peter Coulton -- you don't read Knuth, you study it.
For me, and my work... Purely Functional Data Structures is great for thinking and developing with functional languages in mind.
"The World is Flat" by Thomas Friedman.
Excellence in programming demands an investment of mental energy and a dedication to continued learning comparable to the professions of medicine or law. It pays a fraction of what those professions pay, much less the wages paid to the mathematically savvy who head into the finance sector. And wages for constructing code are eroding because it's a profession that is relatively easy for the intelligent and self-disciplined in most economies to enter.
Programming has already eroded to the point of paying less than, say, plumbing. Plumbing can't be "offshored." You don't need to pay $2395 to attend the Professional Plumber's Conference every other year for the privilege of receiving an entirely new set of plumbing technologies that will take you a year to learn.
If you live in North America or Europe, are young, and are smart, programming is not a rational career choice. Businesses that involve programming, absolutely. Study business, know enough about programming to refine your BS detector: brilliant. But dedicating the lion's share of your mental energy to the mastery of libraries, data structures, and algorithms? That only makes sense if programming is something more to you than an economic choice.
If you love programming and for that reason intend to make it your career, then it behooves you to develop a cold-eyed understanding of the forces that are, and will continue, to make it a harder and harder profession in which to make a living. "The World is Flat" won't teach you what to name your variables, but it will immerse you for 6 or 8 hours in economic realities that have already arrived. If you can read it, and not get scared, then go out and buy "Code Complete."
This last year I took a number of classes. I read
The Innovator's Dilemma (disruptive tech)
The Mythical Man Month (managing software)
Crossing the Chasm (startup)
Database Management Systems, The COW Book
Programming C#, The OSTRICH Book
Beginning iPhone Developmen, The GRAPEFRUIT Book
Each book was amazing but the Innovator's Dilemma by Clayton Christensen (1997!!!) is really a fantastic book, and it got me really thinking about the modern software world. The challenge addressed is disruptive technology, and how disk drive companies and non-technical companies are always disrupted by new, game changing technology. It gives one a new perspective when thinking about Google, probably the biggest 'web' company. Why do they have their hands in EVERYTHING? It's because they don't want to have their position disrupted by something new. The preview on google is plenty to get the idea. Read it!
hackers, by Steven Levy.
The personality and way of life must come first. Everything else can be learned.
The Practice of Programming
and
How to solve it by computer
alt text http://img.infibeam.com/img/7101e0ee/496b1/05/629/P-M-B-9788131705629.jpg?hei=200&wid=160&op_sharpen=1
The Python language was very influential to me, I wish I would have read these book years ago. The beauty and simplicity of the Python language really affected how I wrote code in other languages.
The New Turing Omnibus http://ecx.images-amazon.com/images/I/51HlYd-%2BRwL._BO2,204,203,200_PIsitb-sticker-arrow-click,TopRight,35,-76_AA300_SH20_OU01_.jpg
Really good book. Has a high-level taste of the most important areas of computer science. Yes, CS != programming, but this is still useful to every programmer.
Object Oriented Analysis and Design with Applications by Brady Booch
The Mythical Man-Month by Fred Brooks
http://en.wikipedia.org/wiki/The_Mythical_Man-Month
I think that "The Art of Unix Programming" is an excellent book, by an excellent hacker/brilliant mind as Eric S. Raymond, who tries to make us understand a few principles of software design (simplicity mainly). This book is a must for every programming who is about to start a project under Unix platform.
While I agree that many of the books above are must-reads (Pragmatic Programmer, Mythical Man-Month, Art of Computer Programming, and SICP come to mind immediately), I'd like to go in a slightly different direction and recommend A Discipline of Programming by Edsger Dijkstra. Even though it's 32 years old, the emphasis on "design for verifiability" is highly relevant (even if "verifiability" means "proof" instead "unit tests").
Code Craft by Pete Goodliffe is a good read!
Code Craft http://ecx.images-amazon.com/images/I/51WZ9AEC3GL._SL500_BO2,204,203,200_PIsitb-dp-500-arrow,TopRight,45,-64_OU01_AA240_SH20_.jpg
Martin Fowler's Refactoring: Improving the Design of Existing Code has already been listed. But I will detail why it has impacted me.
The essence of the whole book is about structuring code so that it is simpler to read and understand by humans. It teaches me strongly that the code that I write is meant for my colleagues and successors to consume and possibly learn something good out of it. It inspires me to consciously program in a manner that leaves people praising my name, and not cursing me to damnation for all eternity.
alt text http://ecx.images-amazon.com/images/I/61dECNkdnTL._SL500_AA240_.jpg
C++ How to Program It is good for beginner.This is excellent book that full complete with 1500 pages.
Here's an excellent book that is not as widely applauded, but is full of deep insight: Agile Software Development: The Cooperative Game, by Alistair Cockburn.
What's so special about it? Well, clearly everyone has heard the term "Agile", and it seems most are believers these days. Whether you believe or not, though, there are some deep principles behind why the Agile movement exists. This book uncovers and articulates these principles in a precise, scientific way. Some of the principles are (btw, these are my words, not Alistair's):
The hardest thing about team software development is getting everyone's brains to have the same understanding. We are building huge, elaborate, complex systems which are invisible in the tangible world. The better you are at getting more peoples' brains to share deeper understanding, the more effective your team will be at software development. This is the underlying reason that pair programming makes sense. Most people dismiss it (and I did too initially), but with this principle in mind I highly recommend that you give it another shot. You wind up with TWO people who deeply understand the subsystem you just built ... there aren't many other ways to get such a deep information transfer so quickly. It is like a Vulcan mind meld.
You don't always need words to communicate deep understanding quickly. And a corollary: too many words, and you exceed the listener/reader's capacity, meaning the understanding transfer you're attempting does not happen. Consider that children learn how to speak language by being "immersed" and "absorbing". Not just language either ... he gives the example of some kids playing with trains on the floor. Along comes another kid who has never even SEEN a train before ... but by watching the other kids, he picks up the gist of the game and plays right along. This happens all the time between humans. This along with the corollary about too many words helps you see how misguided it was in the old "waterfall" days to try to write 700 page detailed requirements specifications.
There is so much more in there too. I'll shut up now, but I HIGHLY recommend this book!
Masters of doom. As far as motivation and love for your profession go: it won't get any better than what's been described in this book, truthfully inspiring story!