Conversa Overview

Conversa Overview

Conversa System Pipeline

Tokenization Polygram Analysis

Collocation Discovery

Co-Occurrence Matrix

Disambiguation & Splitting

Term Clustering

Automatic Annotation

Text Synthesis

Raw text Expressions Centers (n=1)

Centers (n=1)

Term clusters

Splits Co-occurrence Vectors

TokensSurroundsCentersOcc. CountsTerm Clusters

Tokenization





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters


Tokens/TypesSurroundsCentersOcc. CountsTerm Clusters

He acted awful mysterious like, and finally he asks me if I'd like to own half of a big nugget of gold. I told him I certainly would."

"And then?" asked Sam, as the old miser paused to take a bite of bread and meat.

Tokenization





Term Clustering


Text Synthesis


Centers (n=1)

Term clusters



Type Count Classand 3 ALPHABETICof 3 ALPHABETIC" 3 PUNCTUATIONhe 2 ALPHABETIC, 2 PUNCTUATIONa 2 ALPHABETICi 2 ALPHABETICacted 1 ALPHABETICawful 1 ALPHABETIC

…

TokenDoc ID

Expr ID Index

he 12 121 1acted 12 121 2awful 12 121 3mysterious 12 121 4like 12 121 5, 12 121 6and 12 121 7

…

Polygram Analysis





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters



121. <start> he acted awful mysterious like , and finally he asks me if i'd like to own half of a big nugget of gold .

122. <start> i told him i certainly would . 123. “ <start> and then “ ? 124. <start> asked sam , as the old miser

paused to take a bite of bread and meat .

<start>_acted

he

Polygram Analysis





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters






he_awful

acted

Polygram Analysis





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters






acted_mysterious

awful

Polygram Analysis





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters






a_ofbite





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters



Rank Surrounds Count1 the_of 77,611 2 <root>_the 42,114 3 the_and 37,105 4 <root>_was 22,769

…21 to_and 8,633 22 to_a 8,471 23 the_in 8,433 24 <root>_said 8,072 25 the_that 7,993

…57 of_. 4,966

…71 a_. 4,493







Term Clustering


Text Synthesis


Centers (n=1)

Term clusters






a_.

big nugget of gold






Term Clustering


Text Synthesis


Centers (n>1)

Term clusters






of_.

bread and meat

Constructing Co-Occurrence Matrix





Term Clustering


Text Synthesis


Centers (n=1)

Term clusters




…

Rank Type Count Class

1 the 626,106 ALPHABETIC

2 . 559,662 TERMINATOR

3 and 364,522 ALPHABETIC

4 to 284,993 ALPHABETIC

5 of 257,403 ALPHABETIC…

Center Surrounds Doc Expr Indexit <start>_is 3 12 1is it_not 3 12 2

not is_for 3 12 3for not_want 3 12 4

want for_of 3 12 5of want_trouble 3 12 6

trouble of_that 3 12 7

Constructing Co-Occurrence Matrix





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters




…

Rank Type Count Class

1 the 626,106 ALPHABETIC

2 . 559,662 TERMINATOR

3 and 364,522 ALPHABETIC

4 to 284,993 ALPHABETIC

5 of 257,403 ALPHABETIC…

Center Surrounds Doc Expr Indexit <start>_is 3 12 1is it_not 3 12 2

not is_for 3 12 3for not_want 3 12 4

want for_of 3 12 5of want_trouble 3 12 6

trouble of_that 3 12 7

Surrounds Rank 1 2 3 4 5 6

684 685

1,839 1,840

Center Rank

Type

the_of

the_and

<start>_the

a_of

<start>_was

and_the

day_the

<start>_may

top_the

you_find

1 the 1 2 of 24 28 9 47 3 to 36 15 1 1

4 and 154 9 7 2

36 my

37 when 112 35 3 1 38 so 19 3 11 39 were 40 which 7 3 41 would 2

173 let 1 3

174 days 11 175 sheldon 10 176 part 29 24

Disambiguation and Split





Term Clustering


Text Synthesis


Centers (n>1)

Term clusters



days

part






Term Clustering


Text Synthesis


Centers (n>1)

Term clusters



Associative

Non-associative

Non

-ass

ocia

tive






Term Clustering


Text Synthesis


Centers (n>1)

Term clusters



Associative

Non-associative

Non

-ass

ocia

tive

Associativedays Non-Associative

Associativepart Non-Associative

Zero Counts

Zero Counts

the_of

early_

offirst

_of… three_ofa_

of… of_things

she_no

there_be

great_

to

…






Term Clustering


Text Synthesis


Centers (n>1)

Term clusters



Associative

Non-associative

Non

-ass

ocia

tive

…

Associativedays Zero Counts

Associativepart Zero Counts

days Non-Associative Zero CountsZero Counts

Splitspart Non-Associative Zero CountsZero Counts

Associativedays Non-Associative Zero Counts

Raw text Expressions Centers (n=1)Associativepart Non-Associative Zero Counts






Term Clustering


Text Synthesis


Centers (n>1)

Term clusters








Term Clustering


Text Synthesis


Centers (n>1)

Term clusters



Tokenization

Polygram Analysis


• Collocation: a multi-word expression that correspond to some conventional way of saying things. – Non-Compositionality– Non-Substitutionality– Non-Modifiability

• Current Methods– Word Counts on Span– Word-to-Word Comparison

• Assumption of Independence

Collocation Discovery using Stop Words

• High-frequency stop words carry very little semantic content but indicate grammatical relationships with other words

• Can be used to delimit collocations:

Example: frequently occurring stop words {a, in} can detect noun phrases:

"Start the buzz-tail," said Cap'n Bill, with a tremble in his voice.

There was now a promise of snow in the air, and a few days later the ground as covered to the depth of an inch or more.

More examples of collocation discovery…

SurroundsU_V

POS Two-Word Collocation Candidate Examples

the_of Noun sordid appetite for dollars, or the dreary existence of countrythe_and Noun alone on a church-top, with the blue sky and a few tall pinnacles

a_of Noun They heard a faint creaking of the flooring

and_the Verb

Preposition

of a lookout, and would visit the adventurers again the next day.

they sailed in and out over the great snow-covered peaks

as_as Adverb the Rover boys became as light hearted as ever.

the_. Noun following day to join him at the Tavistock Hotel .

and_and Verb

Adjective

Noun

tried not to show it, and sang songs and cheered its opponents.

was [..] quite broad and led upward and in the general direction in snowballing each other and Jack Ness and Aleck Pop.

Definitions

Triplet UVW: combined predecessor, center, and successor of three or more words contained within a sentence

Surrounds: any observed pairing of predecessor and successor words enclosing one or more centers

encloses centers .


Step 1: discover surrounds from the corpus, count their occurrences, and rank order them from highest to lowest occurrence count

Step 1: Collocation Discovery

Step 2: Select SurroundsSelect top k from the rank-ordered surrounds satisfying the surrounds total proportionality criterion

Example: υ=25%, the top 1,848 surrounds are selected for collocation candidate extraction

Step 3: Extract Collocation CandidatesAlgorithm ExtractCollocationCandidates(S,𝔖𝜐) 𝔙∶= ∅ for all expressions in corpus: ∀ 𝑠 ∈𝑆 for all selected surrounds: ∀𝜑 ∈𝔖𝜐 for word indices in s: 𝑖 = 1 𝑡𝑜 ȁ�𝑠ȁ� if word si=predecessor: 𝑠𝑖 = 𝑈𝜑 for word indices in s: 𝑗= 𝑖 + 3 𝑡𝑜 ȁ�𝑠ȁ� if word s[j]=predecessor 𝑗= 𝑊𝜑 Add to collocations candidates:

𝔙∶= 𝔙∪ ራ 𝑠𝑘𝑗−1

𝑘=𝑖+1

Return candidate list, 𝔙

Step 3: Extract Collocation Candidates

Here it leaped [..],

just as a wild beast in

captivity paces angrily [..] .

<Start>

Given Surrounds {a, in}, discover Collocation Candidates:

It was the one that seemed to

have had a hole bored in it and

then plugged up again .

Step 3: Extract Collocation Candidates

<Start>

hole bored is not really a good collocation!

Step 4. Select CollocationsApply a non-parametric variation of a frequently applied method to determine which collocation candidates co-occur significantly more often than chance.

The null hypothesis assumes words are selected independently at random and claims that the probability of a collocation candidate V is the same as the product of the probabilities of the individual words

𝐻0 :𝑃 (𝑉 )=∏𝑖=1

𝑛

¿¿

Step 4. Select Collocations

• Sample variance s2=P(V) is based on the assumption that the null hypothesis is true

• Selection of a word is essentially a Bernoulli trial with parameter P(V), with sample variance s2=P(V)(1-P(V)) and mean µ=p.

• Since P(V) << 1.0, s2 ≈P(V).

𝑍= 𝑋−𝜇

√ 𝑠2

𝑛

=𝑃 (𝑉 )−∏𝑖=1

𝑛

¿¿¿

Use of the Empirical CDFEmpirical CDF approximate of the true, but unknown distribution, FN:

Confidence Bounds for the Empirical CDFDvoretzky, Kiefer and Wolfowitz (DKW) Inequality provide a method of computing the upper an lower confidence bound for an empirical CDF given a type-I error probability α and the total number of instances within the sample N:

The upper and lower bounds of the empirical CDF can then be calculated:

and

The Gutenberg Youth Corpus with N = 9,743,797 and a selected α=0.05, provides for a very tight uncertainty bound of 95% ± 0.04% and a critical value of 2.51.

Results: Some Accepted CollocationsAccepted Collocation Candidate (α=0.05) Occurrence count

Collocation Confidence

there was 3685 100%don`t know 1181 100%Young Inventor 967 100%Rover Boys 674 100%went on 1295 100%Mr. Damon 537 100%Emerald City 276 99%little girl 407 99%at once 592 99%other side of 299 99%steam yacht 137 98%two men 204 98%was decided 212 97%long ago 99 97%pilot house 86 97%Von Horn 80 97%the living room 41 95%beg your pardon 38 95%quickly as possible 39 95%

Results: Some Rejected Collocations

Rejected Collocation Candidate Occurrence countCollocation Confidence

corner of the house 19 93%may be added here 18 93%as quickly as possible 18 93%late in the afternoon 18 93%she was glad 25 92%have you a member 2 78%paper to his eyes 2 78%quietly drew 2 49%It’ll do 2 32%the chemist’s 2 32%rob began 2 16%you explain 6 16%surprised that 19 16%children might 3 16%been the first 13 16%did i know 2 0%or the 3 0%and that the 2 0%and in the 8 0%


SplittingProblem: Solve a multi-membership clustering problem where:

– Targets, t, belong to one or more classes, C– All Targets have N Feature Vectors, ft , of occurrence counts [0, ∞) – Class membership is indicated uniquely by one or more feature vectors– Feature Vectors are noisy – random counts may occur that are false

indicators of actual class membership

Objective: Cluster targets by class membership, such that each class forms a distinct, homogeneous sub-tree and each target is placed in class-clusters representing the complete target’s class membership (i.e. a target must appear in one or more class-clusters)

ExampleGiven five words, generate a clustering by POS class membership using surrounds (words before and after) feature counts

Target Class Membership

Eat Verb

Run Noun, Verb

Test Noun, Verb, Adjective

Wise Noun, Adjective

Quickly Adverb

Eat Run Test WiseQuickly

Run Test Test Wise

Splitting Feature Vectors

Fundamental measure of distance is the Pearson Product-Moment Correlation Coefficient:

and and

When most features have counts near 0, if both zA,i , zB,i are > 0, then feature fi strengthens the correlation of A, B

Define fi an correlative feature for target A and B, if zA,i , zB,i > 0; otherwise, fi is defined non-correlative

Split f into two vectors of length N: correlative f (a) and Non-correlative f (n) : if is correlative, , and 0otherwise, 0

Dealing with Noisy Features

Need a statistical test to separate noisy correlative from non-correlative features:Assume a random process is uniformly inserting counts that do not indicate class membershipA Non-Correlative B: AB

A Correlative B: AB

Perform Test on each fi given Type I Error Probability αAssume zA,i , zB,i ~N(0,1) [assumption of normality seems weak, since the distribution of z is so highly skewed – perhaps applying a geometric est. would be more appropriate]

Null Hypothesis H0: fi is non-correlative zA,i = 0 or zB,i = 0

Alt Hypothesis Halt: fi is correlative zA,i > 0 and zB,i > 0

H0 Rejection Region: zA,i > zα and zB,i > zα

Test ExampleFour distinct classes with correlative features:

Three Targets with counts from each class:

Additional Up to 10% noise is distributed uniformly on all N=1000 features

Class CA Class CB Class CC Class CD

Target t1 2,000 500 500

Target t2 1,000 500

Target t3 600 1000

Phi -- Class Probability Distributions: P(fi) in %1 2 3 4 5 6 7 8 9 10 11 12

CA 25 25 50

CB 33 33 33

CC 30 20 50

CD 30 20 50

Correlative Features Detected at α=20%

-2 0 2 4 6 8-1

0

1

2

3

4

5

6

7

8

1 2 3

4 5

6

7 8 9

10

11

12

13 14 15 16 17

18

19 20 21 22 23 24 25 26 27

28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

43 44 45

46

47

48 49 50 51 52 53 54 55 56

57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

73 74 75 76

77 78 79 80 81 82 83 84 85 86 87

88 89 90 91 92

93 94 95 96 97 98 99 100

Target 1 Z-scores

Targ

et 2

Z-s

core

s

-2 0 2 4 6 80

10

20

30

40

50histogram of Z-scores

-2 0 2 4 6-1

0

1

2

3

4

5

6

7

1 2

3

4 5 6

7 8

9

10 11 12 13

14 15

16

17

18 19

20 21

22 23 24 25 26 27 28 29 30 31 32 33 34 35

36 37 38 39

40 41 42 43 44 45 46 47

48 49 50 51 52 53 54 55 56 57 58 59 60 61

62

63 64 65 66 67 68 69 70 71

72 73 74 75 76 77 78 79 80

81

82 83 84

85 86 87 88 89 90

91 92 93 94 95 96 97

98 99 100

Target 2 Z-scores

Targ

et 3

Z-s

core

s

-2 0 2 4 6 80

10

20

30

40

50Histogram of z-scores

-2 0 2 4 6 8-1

0

1

2

3

4

5

6

7

8

1 2

3

4 5 6

7 8

9

10 11 12 13 14 15

16 17

18 19 20 21 22 23

24 25 26 27 28 29 30 31 32 33 34 35

36 37 38 39 40 41

42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

62 63 64 65 66 67 68 69 70 71

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

99 100

Target 1 Z-scores

Targ

et 3

Z-s

core

s

-2 0 2 4 6 80

10

20

30

40

50Histogram of z-scores

Features f10 and f12 from Class CD detected associating Targets t1 and t2

No association detected between Targets t2 and t3

Features f7 and f8 from Class CC, and f1, f2, and f3 from CA detected associating Targets t1 and t3

Term Clustering

BibliographyBaldwin, T., Kordoni, V., Villavicencio, Al., (2009) Prepositions in Applications: A Survey and Introduction to the Special Issue, Association for Computational Linguistics Volume 38, Number 2

Entity Extraction 04053148: High-Performance Unsupervised Relation Extraction from Large Corpora, Rozenfeld, Felman, ICDM’06

Unsupervised relation discovery based on clusteringUsed small number of existing relationship patterns

05743647: Extracting Descriptive Noun Phrases From Conversational Speech, BBN Technologies, 2002Used the BBN Statistically-derived Information from Text (SIFT) tool to extract noun phrases,

combined with speech recognition, using the Switchboard I tagged corpus

05340924: Named-Entity Techniques for Terrorism Event Extraction and Classification, 2009Thai Language, features derived from the terrorism gazetteer, terrorism ontology, and terrorism

grammar rule, TF-IDF distance for some standard machine learning algorithms (k-neares, SVM, Dtree)

05484737: Text analysis and entity extraction in asymmetric threat response and predictionUse of named entities lexicon and fixed bigrams to extract entities – refer to NIST Automated

Content Explorer (ACE)

Bibliography05484763: Unsupervised Multilingual Concept Discovery from Daily Online News Extracts, 2010

Applies Left and Right context to extract multi-word key terms, then applies hierarchical clustering for concept discovery

News corpus was obtained using RSS feed application called: TheYolk

0548765: Entity Refinement using Latent Semantic Indexing, Agilex, 2010starts with “state-of-the-art” commercial entity extraction software, create LSI representation

multiword text blocks, query LSI space for raked list of major terms

047-058: Evaluation of Named Event Extraction SystemsJava Entity Extractors: Annie, Lingpipe10 evaluated systems indicate problems extracting noun phrases Addresses deficiencies with conferences for NERC: CON-LL, MUC, NIST ACE / Text Analysis Conf:

http://www.nist.gov/tac/2012/KBP/

2002-coling-names –pub: Unsupervised Learning of Generalized Names, 2002Uses patterns from seed terms to learn new terms (but apparently not the surrounds)Seeks to identify generalized names from medical corpora, like “mad cow disease”




Bibliographyqatar-bhomick: Rich Entity Type Recognition in Text, 2010

word-based context-based tagger using perceptron-trained HMM

Collocation Extraction beyond Independence Assumption, ACL 2010Extracting collocations using PMI and Aggregate Markov ModelsGerman collocation gold standardVery low precision results reported

Aromatically Extracting and Representing collocations for language generation (1998), Smadja, McKeownStock Market collocations

An extensive empirical study of collocation extraction methods, ACL Student Research Workshop, 200587 features of similarity for collocationsperformance measured in Precision, Recall

Documents

Conversa Overview