Automated Identification of Preposition Errors

Joel TetreaultEducational Testing Service

ECOLTOctober 29, 2010

Outline

• Computational Linguistics (CL) and Natural Language Processing (NLP)

• NLP at ETS (automated scoring)• Automated Preposition Error Detection

Linguistics

D’oh!

Computational Linguistics

D’oh!

Want computers to understand language

Computational Linguistics

D’oh!

Computational Linguistics vs. NLP

• Computational Linguistics (CL):– Computers understanding language– Modeling how people communicate

• Natural Language Processing (NLP):– Applications on the computer side– Natural: refers to languages spoken by people

(English, Swahili) vs. artificial languages (C++)– Take CL theories and implement them into tools

• CL and NLP often conflated

Computational Linguistics Space

• Computer Science: learning algorithms• Linguistics: formal grammars • Psychology: human processing modeling

CL

Computational Linguistics Space

CL

ArtificialIntelligence

Intelligent Machines

• Perfect speech recognition• Perfect language understanding• Perfect speech synthesis• Perfect discourse modeling• Intention Recognition• World Knowledge• (Vision)

Real World Applications of NLP

• Spelling and Grammar correction/detection– MSWord, e-rater

• Machine Translation– Google and Bing Translate

• Opinion Mining– Extract sentiment of demographic from blogs and

social media

• Speech Recognition and Synthesis• Automatic Document Summarization

NLP at ETS: Motivation

• Millions of GRE and TOEFL tests taken each year

• Tests move to more natural assessment– Fewer multiple choice questions– Tests have essay component

• Problem:– Thousands of raters required – Costly and timely

NLP at ETS

• Use NLP techniques to automatically score essays (e-rater)

• Other scoring tools which use NLP:– Criterion: online writing feedback– SpeechRater: automatic speaking assessment– C-Rater: content scoring of short answers– Plagiarism Detection

E-rater (Automated Essay Scoring)

• First deployed in 1999 for GMAT Writing Assessment

• Operational for the GRE and TOEFL as well as a collection of smaller assessments

• System Performance (5 point essay scale):– E-rater/Human agreement: 75% exact, 98% exact

(+1 adjacent)– Comparable to two humans

E-rater (Automated Essay Scoring)

• Massive collection of 50+ weighted features organized into 5+ high level features

• Each feature is represented by a module:– Simple: collection of manual rules and/or regular

expressions– More complex: NLP (Natural Language Processing)

statistical system is behind the feature

• Combined using linear regression

E-rater Features

E-rater Features

How to Game the System

• Word Salad Detector

• Unusually Short / Off-Topic Essays

“Quick The the over brown dogs fox. Jumped. Lazy”

“Skfhdorla;sf[e’skas as,fr’r;/.,fkrasa”

“I don’t know how to explain this question because I took a nap. Sorry.” “I THINK EVERYONE SHOULD BE ABLE TO WEAR WHATEVER THE HELL THEY WANT TO WEAR.”

NLP for English Language Learners

• Increasing need for tools for instruction in English as a Second Language (ESL)– 300 million ESL learners in China alone– 10% of US students learn English as a second

language– Teachers now burdened with teaching classes with

wildly varying levels of English fluency– Assessments for EFL Teacher Proficiency

NLP for English Language Learners

• Other Interest:– Microsoft Research (ESL Assistant)– Publishing/Assessment Companies (Cambridge, Oxford,

Pearson)– Universities

Objective

• Research Goal: develop NLP tools to automatically provide feedback to ESL learners about grammatical errors

• Preposition Error Detection– Selection Error (“They arrived to the town.”)– Extraneous Use (“They came to outside.”)– Omitted (“He is fond this book.”)

Motivation

• Preposition usage is one of the most difficult aspects of English for non-native speakers – [Dalgish ’85] – 18% of sentences from ESL essays

contain a preposition error– Our data: 8-10% of all prepositions in TOEFL essays

are used incorrectly

Why are prepositions hard to master?

• Prepositions are problematic because they can perform so many complex roles– Preposition choice in an adjunct is constrained by

its object (“on Friday”, “at noon”)– Prepositions are used to mark the arguments of a

predicate (“fond of beer.”)– Phrasal Verbs (“give in to their demands.”)

• “give in” “acquiesce, surrender”

Why are prepositions hard to master?

• Multiple prepositions can appear in the same context:

“When the plant is horizontal, the force of the gravity causes the sap to move __ the underside of the stem.”“When the plant is horizontal, the force of the gravity causes the sap to move __ the underside of the stem.”

Preposition Error Detection

• In NLP: computer system learns from lots and lots of data

• Training Phase: Create a “model” of the problem area– Face detection– Credit Card Usage– Translating from Chinese to English

• Testing Phase: Use model to classify new cases

Baseball Feature Example

• Predict the outcome of the baseball game• Look at all the games where both teams

played each other:• For each game (event), use features:

– Win/loss records before game– Home field advantage– Players’ prior performance

• Train learning algorithm

Baseball Feature Example

Event Winner Location Prior Isotopes Win Streak

Prior Capital City Win Streak

Game 1 Isotopes Springfield 0 3

Game 2 Capital City Springfield 4 0

Game 3 Capital City Capital City 2 0

Game 4 Isotopes Springfield 2 1

Building a Model of Preposition Usage

• Prepositions are influenced by:– Words in the local context, and how they interact

with each other (lexical)– Syntactic structure of context– Semantic interpretation

• Get computer to understand correct usage:– Encode these influences as “features”– Train computer algorithm on millions of examples

of correct usage with the associated features

Deriving the Features

• Derived using NLP tools• Tokenizing

– “He is fond of beer . ”

• Part-of-Speech Tagging– “ He_PRP is_BE fond_VB of_PREP beer_NN ._.”

• Chunking / Parsing– “ {NP He_PRP } {VP is_BE fond_VB } of_PREP {NP

beer_NN } ._.”

Feature Overview

• System uses a minimum of 25 features– Lexical, syntactic, semantic sources– Head words before and after preposition– Words in the local context (+/- 2 words)– Part of Speech (POS) of words above– Combination Features– Parse Features

Preposition Feature Example

Event Prep Prior Verb Prior Noun Following Word

POS of Following Word

Prep 1 of fond <none> beer NN

Prep 2 at arrive <none> the Det

Prep 3 with <none> car the Det

1. He is fond of beer.2. The train will arrive at the Springfield Station.3. The car with the broken wheel is in the shop.

Flagging Errors

• Train learning algorithm on millions of events develop model (classifier)

• Testing (flagging errors)– Derive features– Replace writer’s preposition with all other

prepositions, classifier outputs score for each preposition

– Compare top scoring preposition to score of writer’s preposition

Thresholds

0

10

20

30

40

50

60

70

80

90

100

of in at by with

“He is fond with beer”

FLAG AS ERROR

Thresholds

0

10

20

30

40

50

60

of in around by with

“My sister usually gets home by 3:00”

FLAG AS OK

Performance

• Evaluation corpus of 5600 TOEFL essays (8200 prepositions)– Each preposition manually annotated

• Recall = 0.19 ; Precision = 0.84– 1/5 of errors are flagged– 84% of flagged errors are indeed errors

• Precision > recall to reduce false positives• State of the Art performance

Conclusions

• Presented an overview of:– NLP– NLP at ETS– One feature (Prepositions) in e-rater

• Future Directions– Use of large scale corpora (WWW)– L1-specific models– Train on error-annotated data

Plugs

• ETS/NLP Publications: – http://ets.org/research/erater.html

• 5th Workshop on Innovative Use of NLP for Educational Applications (NAACL-10)– http://www.cs.rochester.edu/u/tetreaul/naacl-bea5.html

Plugs

• “Automated Grammatical Error Detection for Language Learners” – Leacock et al., 2010– Synthesis Series

Thanks!

Joel Tetreault: JTetreault@ets.org