For Linguists who Want to be Linguists, not Computer Scientists

Launching a Career in Tech

Jeff Lilly, Research Scientist at Amazon

Background

MA in Linguistics from UMD College Park, 2000Who I’ve worked for:

3 start-ups, 2 defense contractors, 1 large speech company (Nuance), and Amazon

What I’ve worked on: Search engines Text understanding & analysis Information extraction Dictation software Lexical databases of various kinds Speech technology Multiple patent applications

Jeff Lilly, Research Scientist at Amazon

The Foolproof Plan

Getting into tech means being the right person in the right place at the right time The right person: has the skills for the job The right place (metaphorically): located strategically

on the social and professional networks of the industry The right time: exactly when the company needs you

So it’s a simple 3-step plan:1. Become the right person2. Get to the right place3. Wait there, shouting and waving occasionally, until

the right time comes

Jeff Lilly, Research Scientist at Amazon

A Dearth

There are not enough linguists in the tech industry. We’re needed.

Computer scientists and systems engineers are building systems that work, oftentimes with almost no input from linguists

But they could build things faster, better, cheaper with our help The speech industry is swarming with brilliant computer scientists who don’t

know what an allophone is Military intelligence analysis systems are built by people who have never heard of

the Binding Theory of coreference

But too many of us can’t code. Half a dozen excellent linguists with PhDs applied to work at our lab at

Amazon in the past year. All were turned away because of inadequate coding ability.

This should change. Linguists want to be linguists, not computer scientists. But if you can pick up

some crucial skills, you can open doors in the tech industry AND make yourself a better linguist.

Jeff Lilly, Research Scientist at Amazon

Coding Fundamentals

Computer Science Basics: Algorithms: searching, sorting, hash tables… Object oriented design principles Mathematical libraries Performance / speed / memory tradeoffs You can get a lot of this from a few courses in Java or

C++Scripting & Unix Environment

Python, Perl Bash Free online courses available for these

Jeff Lilly, Research Scientist at Amazon

Serious Coding

Corpus Processing & Analysis NLTK (Natural Language Toolkit – Python) – tokenization,

chunking, etc. Linguistic Data Consortium WordNet, FrameNet, Google Ngrams, UPenn’s Eigenwords, etc.

Machine Learning Andrew Ng’s Stanford online course: highly recommended as a

broad survey of techniques applicable across all kinds of problems

Find other coursework specific to what you want to focus on Phonetics, phonology: speech recognition and production (HMM’s,

GMM’s, DNN’s, ngram LM’s) Syntax, semantics: information extraction, dialogue systems,

machine translation (CRF’s, statistical parsing)

Jeff Lilly, Research Scientist at Amazon

Get Experience

Don’t just take courses. Use this in your research. Aim to publish work that shows you

can code, you can process and analyze corpora, and you can build machine learning systems.

Examples of Using Machine Learning for Linguistics: Build a corpus of Pittsburgh English, and build a system to

compare it to a General American corpus to find distinctive dialect features

Break up a corpus of spoken language by dialect, register, etc.

Use the eigenword corpus and FrameNet to explore the relationship between verb distribution and argument structure

Jeff Lilly, Research Scientist at Amazon

The Right Place, the Right Time

Meet People I’m terrible at this, but it’s a learnable skill; been hired 5 times, and 3

times it was because of a network connection There are tools that help: LinkedIn is a big deal – get on it!

It has revolutionized tech recruiting in just the last few years Conferences continue to be essential

Other Notes Linguist list has many jobs, but lots of potential employers haven’t

heard of it Job search sites (monster, careerbuilder, indeed) are good too,

particularly for defense industry Job market is always in flux; if you don’t like it now, wait a month.

Companies don’t always want people like us, but when they do, they want us now and they want us bad

As in almost all industries, it’s easier to find a job if you’re willing to move

Jeff Lilly, Research Scientist at Amazon

A Case Study

For three years we turned down excellent linguists with PhDs but lackluster coding ability

A few weeks ago, we hired an excellent linguist with an MA and solid coding skills Halfway through her Master’s program in computational

linguistics, she came and interned with us for a few months Pluses: Java, OO, scripting, algorithms, corpus work

(nothing spectacular, but solid) Minuses: no machine learning She had a lot to learn still, but she worked hard and we

gave her a job offerWe need more people like this

Jeff Lilly, Research Scientist at Amazon

Wrap-Up

The Right Person: Knowledge of coding fundamentals Experience with machine learning and corpus analysis These skills help you in linguistics AND the tech industry

The Right Place & Time: Networking, patience, persistence Start now:

www.linkedin.com/pub/jeff-lilly/3/932/308/ Thomas Schaaf (local to Pgh):

www.linkedin.com/pub/thomas-schaaf/0/a77/ 34b Amazon is hoping to open an office in town if there is

enough of a local talent pool

Jeff Lilly, Research Scientist at Amazon