Overview of machine learning for nlp tasks part i
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Overview of Machine Learning for NLP Tasks: part I. (based partly on slides by Kevin Small and Scott Yih). Goals of Introduction. Frame specific natural language processing (NLP) tasks as machine learning problems Provide an overview of a general machine learning system architecture

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Overview of Machine Learning for NLP Tasks: part I

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Overview of Machine Learning for NLP Tasks: part I

(based partly on slides by Kevin Small and Scott Yih)


Goals of Introduction

  • Frame specific natural language processing (NLP) tasks as machine learning problems

  • Provide an overview of a general machine learning system architecture

    • Introduce a common terminology

    • Identify typical needs of ML system

  • Describe some specific aspects of our tool suite in regards to the general architecture

    • Build some intuition for using the tools

  • Focus here is on Supervised learning


Overview

  • Some Sample NLP Problems

  • Solving Problems with Supervised Learning

  • Framing NLP Problems as Supervised Learning Tasks

  • Preprocessing: cleaning up and enriching text

  • Machine Learning System Architecture

  • Feature Extraction using FEX


Context Sensitive Spelling[2]

  • A word level tagging task:

    I would like a peace of cake for desert.

    I would like apieceof cake fordessert.

    In principal, we can use the solution to the

    duel problem.

    Inprinciple,we can use the solution to the

    dualproblem.


Part of Speech (POS) Tagging

  • Another word-level task:

    Allen Iverson is an inconsistent player. While he can shoot very well, some nights he will score only a few points.

    (NNP Allen) (NNP Iverson) (VBZ is) (DT an) (JJ inconsistent) (NN player) (. .) (IN While) (PRP he)(MD can)(VB shoot) (RB very)(RB well)(, ,) (DT some) (NNS nights) (PRP he)(MD will) (VB score)(RB only) (DT a) (JJ few)(NNS points)(. .)


Phrase Tagging

  • Named Entity Recognition – a phrase-level task:

    After receiving his M.B.A. from Harvard Business School, Richard F. America accepted a faculty position at the McDonough School of Business (Georgetown University) in Washington.

    After receiving his [MISC M.B.A.] from [ORG Harvard Business School], [PER Richard F. America] accepted a faculty position at the [ORG McDonough School of Business] ([ORG Georgetown University]) in [LOC Washington].


Some Other Tasks

  • Text Categorization

  • Word Sense Disambiguation

  • Shallow Parsing

  • Semantic Role Labeling

  • Preposition Identification

  • Question Classification

  • Spam Filtering

    :

    :


Supervised Learning/SNoW


Binary Classification

Multi-class Classification

Ranking

Regression

{Feature, Instance, Input} Space– space used to describe each instance; often

Output Space – space of possible output labels; very dependent on problem

Hypothesis Space – space of functions that can be selected by the machine learning algorithm; algorithm dependent (obviously)

Learning Mapping Functions


Multi-class Classification[3,4]

One Versus All (OvA)

Constraint Classification


Online Learning[5]

  • SNoW algorithms include Winnow, Perceptron

  • Learning algorithms are mistake driven

  • Search for linear discriminant along function gradient (unconstrained optimization)

  • Provides best hypothesis using data presented up to to the present example

  • Learning rate determines convergence

    • Too small and it will take forever

    • Too large and it will not converge


Framing NLP Problems as Supervised Learning Tasks


Defining Learning Problems[6]

  • ML algorithms are mathematical formalisms and problems must be modeled accordingly

  • Feature Space – space used to describe each instance;

    often Rd, {0,1}d, Nd

  • Output Space – space of possible output labels, e.g.

    • Set of Part-of-Speech tags

    • Correctly spelled word (possibly from confusion set)

  • Hypothesis Space – space of functions that can be selected by the machine learning algorithm, e.g.

    • Boolean functions (e.g. decision trees)

    • Linear separators in Rd


Context Sensitive Spelling

Did anybody (else) wanttoosleep forto more

hours this morning?

  • Output Space

    • Could use the entire vocabulary; Y={a,aback,...,zucchini}

    • Could also use a confusion set; Y={to, too, two}

  • Model as (single label) multi-class classification

  • Hypothesis space is provided by SNoW

  • Need to define the feature space


What are ‘feature’, ‘feature type’, anyway?

  • A feature type is any characteristic (relation) you can define over the input representation.

    • Example: feature TYPE = word bigrams

      Sentence:

      The man in the moon eats green cheese.

      Features:

      [The_man], [man_in], [in_the], [the_moon]….

  • In Natural Language Text, sparseness is often a problem

    • How many times are we likely to see “the_moon”?

    • How often will it provide useful information?

    • How can we avoid this problem?


Preprocessing: cleaning up and enriching text

  • Assuming we start with plain text:

    The quick brown fox jumped over the lazy dog. It landed on Mr. Tibbles, the slow blue cat.

  • Problems:

    • Often, want to work at the level of sentences, words

    • Where are sentence boundaries – ‘Mr.’ vs. ‘Cat.’?

    • Where are word boundaries -- ‘dog.’ Vs. ‘dog’?

  • Enriching the text: e.g. POS-tagging:

    (DT The) (JJ quick) (NN brown) (NN fox) (VBD jumped)

    (IN over) (DT the) (JJ lazy) (NN dog) (. .)


Download Some Tools

  • http::/l2r.cs.uiuc.edu/~cogcomp/

    • Software::tools, Software::packages

  • Sentence segmenter

  • Word segmenter

  • POS-tagger

  • FEX

  • NB: RIGHT-CLICK on “download” link

    • select “save link as...”


Preprocessing scripts

  • http://l2r.cs.uiuc.edu/~cogcomp/

  • sentence-boundary.pl

    ./sentence-splitter.pl –d HONORIFICS –i nyttext.txt -o nytsentence.txt

  • word-splitter.pl

    ./word-splitter.pl nytsentence.txt > nytword.txt

  • Invoking the tagger:

    ./tagger –i nytword.txt –o nytpos.txt

  • Check output


Problems running .pl scripts?

  • Check the first line:

    #!/usr/bin/perl

  • Find perl library on own machine

    • E.g. might need...

      #!/local/bin/perl

  • Check file permissions...

    > ls –l sentence-boundary.pl

    > chmod 744 sentence-boundary.pl


Minor Problems with install

  • Possible (system-dependent) compilation errors:

    • doesn’t recognize ‘optarg’

    • POS-tagger: change Makefile in subdirectory snow/ where indicated

    • sentence-boundary.pl: try ‘perl sentence-boundary.pl’

  • Link error (POS tagger): linker can’t find –lxnet

    • remove ‘-lxnet’ entry from Makefile

    • generally, check README, makefile for hints


The System View


Raw

Text

Formatted

Text

Feature

Extraction

Preprocessing

Machine

Learner

Function

Parameters

Labels

Labels

Inference

Classifier(s)

A Machine Learning System

Feature

Vectors

Training

Examples

Testing

Examples


0 0 0 They

0 0 1 recently

0 0 2 recovered

0 0 3 a

0 0 4 small

piece 0 5 piece

0 0 6 of

:

0 1 6 including

0 1 7 QUOTE

peace 1 8 Peace

0 1 9 in

0 1 10 the

0 1 11 Valley

0 1 12 .

0 1 13 QUOTE

They recently recovered a small piece of a live Elvis concert recording.

He was singing gospel songs, including “Peace in the Valley.”

Preprocessing Text

  • Sentence splitting, Word Splitting, etc.

  • Put data in a form usable for feature extraction


Raw

Text

Formatted

Text

Feature

Extraction

Preprocessing

A Machine Learning System

Feature

Vectors


Feature Extraction with FEX


Feature Extraction with FEX

  • FEX (Feature Extraction tool) generates abstract representations of text input

    • Has a number of specialized modes suited to different types of problem

    • Can generate very expressive features

    • Works best when text enriched with other knowledge sources – i.e., need to preprocess text

      S = I would like a piece of cake too!

  • FEX converts input text into a list of active features…

    1: 1003, 1005, 1101, 1330…

    Where each numerical feature corresponds to a specific textualfeature:

    1: label[piece]

    1003: word[like] BEFORE word[a]


Converts formatted text into feature vectors

Lexicon file contains feature descriptions

0 0 0 They

0 0 1 recently

0 0 2 recovered

0 0 3 a

0 0 4 small

piece 0 5 piece

0 0 6 of

:

0 1 6 including

0 1 7 QUOTE

peace 1 8 Peace

0 1 9 in

0 1 10 the

0 1 11 Valley

0 1 12 .

0 1 13 QUOTE

0, 1001, 1013, 1134, 1175, 1206

1, 1021, 1055, 1085, 1182, 1252

Lexicon

File

Feature Extraction


Role of FEX

  • Why won't you accept the facts?

  • No one saw her except the postman.

Feature ExtractionFEX

1, 1001, 1003, 1004, 1006:

2, 1002, 1003, 1005, 1006:

lab[accept], w[you], w[the], w[you*], w[*the]

lab[except], w[her], w[the], w[her*], w[*the]


Corpus

Example

Lexicon

Script

Four Important Files

A new representation of the raw text data

Feature vectors for SNoW

FEX

Mapping of feature and feature id

  • Control FEX’s behavior

  • Define the “types” of features


Corpus – General Linear Format

  • The corpus file contains the preprocessed input with a single sentence per line.

  • When generating examples, Fex never crosses line boundaries.

  • The input can be any combination of:

    • 1st form: words separated by white spaces

    • 2nd form: tag/word pairs in parentheses

    • There is a more complicated 3rd form, but deprecated in view of alternative, more general format (later)


Corpus – Context Sensitive Spelling

  • Why won't you accept the facts?

    (WRB Why) (VBD wo) (NN n't) (PRP you)(VBP accept) (DT the) (NNS facts) (. ?)

  • No one saw her except the postman.

    (DT No) (CD one) (VBD saw) (PRP her) (IN except) (DT the) (NN postman) (. .)


Script – Means of Feature Engineering

  • Fex does not decide or find good features.

  • Instead, Fex provides you an easy method to define the feature types and extracts the corresponding features from data.

  • Feature Engineering is in fact very important in practical learning tasks.


Script – Description of Feature Types

  • What can be good features?

    • Let’s try some combinations of words and tags.

  • Feature types in mind

    • Words around the target word (accept, except)

    • POS tags around the target

    • Conjunctions of words and POS tags?

    • Bigrams or trigrams?

    • Include relative locations?


Graphical Representation

Window [-2,2]

0

Target

-4

-3

-2

-1

1

2

3

0

1

2

3

4

5

6

7


Script – Syntax

  • Syntax:

    targ [inc] [loc]: RGF [[left-offset, right-offset]]

    • targ– target index

      • If targ is ‘-1’…

        • target file entries are used to identify the targets

        • If no target file is specified, then EVERY word is treated as a target

    • inc– use the actual target instead of the generic place-holder (‘*’)

    • loc– include the location of feature relative to the target

    • RGF– define “types” of features like words, tags, conjunctions, bigrams, trigrams, …, etc

    • left-offset and right-offset: specify the window range


Basic RGF’s – Sensors (1/2)

  • Sensor is the fundamental method of defining “feature types.”

  • It is applied on the element, and generates active features.


Basic RGF’s – Sensors (2/2)

lab: a special RGF that generates labels

  • lab(w), lab(t), …

  • Sensors are also an elegant way to incorporate our background knowledge.

More sensors can be found by looking at FEX source (Sensors.h)


Complex RGF’s

  • Existential Usage

    • len(x=3), v(X)

  • Conjunction and Disjunction

    • w&t; w|t

  • Collocation and Sparse Collocation

    • coloc(w,w); coloc(w,t,w); coloc(w|t,w|t)

    • scoloc(t,t); scoloc(t,w,t); scoloc(w|t,w|t)


(Sparse) Collocation

0

Target

-4

-3

-2

-1

1

2

3

0

1

2

3

4

5

6

7

-1 inc: coloc(w,t)[-2,2]

w[‘t]-t[PRP], w[you]-t[VBP]w[accept]-t[DT], w[the]-t[NNS]

-1 inc: scoloc(w,t)[-2,2]

w[‘t]-t[PRP], w[‘t]-t[VBP], w[‘t]-t[DT], w[‘t]-t[NNS],w[you]-t[VBP], w[you]-t[DT], w[you]-t[NNS], w[accept]-t[DT], w[accept]-t[NNS], w[the]-t[NNS]


Examples – 2 Scripts

  • Download examples from tutorial page:

    ‘context sensitive spelling materials’ link

  • accept-except-simple.scr

    -1: lab(w)

    -1: w[-1,1]

  • accept-except.scr

    -1: lab(w)

    -1: w|t [-2,2]

    -1 loc: coloc(w|t,w|t) [-3,-3]


Generated by lab(w)

Lexicon & Example (1/3)

  • Corpus:

    … (NNS prices) (CC or) (VB accept) (JJR slimmer) (NNS profits) …

  • Script: ae-simple.scr

    -1 lab(w); -1: w[-1,1]

  • Lexicon:

    1 label[w[except]]

    2 label[w[accept]]

    1001 w[or]

    1002 w[slimmer]

  • Example:

    2, 1001, 1002;

Feature indices of lab start from 1.

Generated by w[-1,1]

Feature indices of regular features start from 1001.


We treat only these two words as targets.

Lexicon & Example (2/3)

  • Target file: fex -t ae.targ …

    accept

    except

  • Lexicon file

    • If the file does not exist, fex will create it.

    • If the file already exists, fex will first read it, and then append the new entries to this file.

    • This is important because we don’t want two different feature indices representing the same feature.


Lexicon & Example (3/3)

  • Example file

    • If the file does not exist, fex will create it.

    • If the file already exists, fex will append new examples to it.

    • Only active features and their corresponding lexicon items are generated.

    • If the read-only lexicon option is set, only those features from the lexicon that are present (active) in the current instance are listed.


Now practice – change script, run FEX, look at the resulting lexicon/examples

> ./fex –t ae.targ ae-simple.scr ae-simple.lex short-ae.pos short-ae.ex


Citations

  • F. Sebastiani. Machine Learning in Automated Text Categorization. ACM Computing Surveys, 34(1):1-47, 2002.

  • A. R. Golding and D. Roth. A Winnow-Based Approach to Spelling Correction. Machine Learning, 34:107-130, 1999.

  • E. Allewin, R. Schapire, and Y. Singer. Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. Journal of Machine Learning Research, 1:113-141, 2000.

  • S. Har-Peled, D. Roth, and D. Zimak. Constraint Classification: A New Approach to Multiclass Classification. In Proc. 13th Annual Intl. Conf. of Algorithmic Learning Theory, pp. 365-379, 2002.

  • A. Blum. On-Line Algorithms in Machine Learning. 1996.


Citations

  • T. Mitchell. Machine Learning, McGraw Hill, 1997.

  • A. Blum. Learning Boolean Functions in an Infinite Attribute Space. Machine Learning, 9(4):373-386, 1992.

  • J. Kivinen and M. Warmuth. The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant. UCSC-CRL-95-44, 1995.

  • T. Dietterich. Approximate Statistical Tests for Comparing Supervised Classfication Learning Algorithms. Neural Computation, 10(7):1895-1923, 1998


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