distance functions and ie – 5 william w. cohen cald

Distance functions and IE – 5

William W. Cohen

CALD

Announcements

• Current statistics:– days with unscheduled student talks: 5– students with unscheduled student talks: 3– Projects are due: 4/28 (last day of class)– Additional requirement: draft (for comments)

no later than 4/21

String distance metrics so far...

• Term-based (e.g. TF/IDF as in WHIRL)– Distance depends on set of words contained in both s and t – so sensitive

to spelling errors.– Usually weight words to account for “importance”– Fast comparison: O(n log n) for |s|+|t|=n

• Edit-distance metrics– Distance is shortest sequence of edit commands that transform s to t.– No notion of word importance– More expensive: O(n2)

• Other metrics– Jaro metric & variants– Monge-Elkan’s recursive string matching– etc?

• Which metrics work best, for which problems?

Results - Overall

Combining Information Extraction and Similarity Computations

Krauthammer et al

Background

• Common task in proteomics/genomics: – look for (soft) matches to a query sequence in a

large “database” of sequences.– want to find subsequences (genes) that are

highly similar (and hence probably related)– want to ignore “accidental” matches– possible technique is Smith-Waterman (local

alignment)• want char-char “reward” for alignment to reflect

confidence that the alignment is not due to chance

Background

• Common task in proteomics/genomics: – look for (soft) matches to a query sequence in a

large “database” of sequences.– want to find subsequences (genes) that are

highly similar (and hence probably related)– want to ignore “accidental” matches– possible technique is Smith-Waterman (local

alignment)• want char-char “reward” for alignment to reflect

confidence that the alignment is not due to chance

Smith-Waterman distance

c o h e n d o r f

m 0 0 0 0 0 0 0 0 0

c 1 0 0 0 0 0 0 0 0

c 0 0 0 0 0 0 0 0 0

o 0 2 1 0 0 0 2 1 0

h 0 1 4 3 2 1 1 1 0

n 0 0 3 3 5 4 3 2 1

s 0 0 2 2 4 4 3 2 1

k 0 0 1 1 3 3 3 2 1

i 0 0 0 0 2 2 2 2 1

dist=5

In general “peaks” in the matrix scores indicate highly similar substrings.

Background

• Common task in proteomics/genomics: – look for (soft) matches to a query sequence in a

large “database” of sequences.– possible technique is Smith-Waterman (local

alignment)• want char-char “reward” for alignment to reflect

confidence that the alignment is not due to chance• based on substitutability theory/stats for amino acids

– doesn’t scale well• BLAST and FASTA: fast approximate S-W

BLAST/FASTA ideas

• Find all char n-grams (“words”) in the query string.

• FASTA:– Use inverted indices to find out where these

words appear in the DB sequence– Use S-W only near DB sections that contain

some of these words

BLAST/FASTA ideas

• Find all char n-grams (“words”) in the query string.

• BLAST:– Generate variations of these words by looking

for changes that would lead to strong similarities

– Discard “low IDF” words (where accidental matches are likely)

– Use expanded set of n-grams to focus search

query string

words and expansions

BLAST/FASTA ideas

• Find all char n-grams (“words”) in the query string.• BLAST:

– Generate variations of these words by looking for changes that would lead to strong similarities

– Discard “low IDF” words (where accidental matches are likely)– Use expanded set of n-grams to focus search

• The BLAST program:– Widely used, – Fast implementation, – Supports asking multiple queries against a database at once...– Can one use it find soft matches of protein names (from a

dictionary) in text?

Basic idea:

• Protein database• Query strings• Proposed alignment

(query->database)• Query algorithm:

BLAST

• Biomedical paper• Protein name dictionary• Extracted protein name

(dict. entry->text)• IE system:

dictionaries+BLAST (optimized for this problem)

1) Mapping text to DNA sequences(Q: what sort of char similarity is this?)

2) Optimizing blast

• Split protein-name database into several parts (for short, medium-length, long protein names)– Scoring depends on length of matched string

• Require space chars before and after “short” protein names.

• Manually search (grid search?) for better settings for certain key parameters for each protein-name subdatabase – With what data?

• Evaluate on one review article, 1162 protein names– inter-annotator agreement not great (70-85%)

2) Optimizing blast

2) Optimizing blast

Results

Results

Overall: precision 71.1%, recall 78.8% (optimized)

IE with Dictionaries

Cohen & Sarawagi

Finding names you know about

• Problem: given dictionary of names, find them in email text– Important task beyond email (biology, link analysis,...)– Exact match is unlikely to work perfectly, due to

nicknames (Will Cohen), abbreviations (William C) , misspellings (Willaim Chen), polysemous words (June, Bill), etc

– In informal text it sometimes works very poorly– Problem is similar to record linkage (aka data

cleaning, de-duping, merge-purge, ...) problem of finding duplicate database records in heterogeneous databases.

Finding names you know about

• Problem: given dictionary of names, find them in email text– Exact match is unlikely to work well for

informal text.– Problem is similar to record linkage– Hard to combine state of the art similarity

metrics (as used in record linkage) with state of the art NER system due to representational mismatch:

• Opening up the box, modern NER systems don’t really know anything about names....

IE as Sequential Word Classification

Yesterday Pedro Domingos spoke this example sentence.

Person name: Pedro Domingos

A trained IE systemmodels the relative probability of labeled sequences of words.

To classify, find the most likely state sequence for the given words:

Any words said to be generated by the designated “person name”state extract as a person name:

person name

location name

background

IE as Sequential Word Classification

Modern IE systems use a rich representation for words, and clever probabilistic models of how labels interact in a sequence, but do not explicitly represent the names extracted.

wt -1

wt

Ot

wt+1

Ot +1

Ot -1

identity of wordends in “-ski”is capitalizedis part of a noun phraseis in a list of city namesis under node X in WordNetis in bold fontis indentedis in hyperlink anchorlast person name was femalenext two words are “and Associates”

…

…part of

noun phrase

is “Wisniewski”

ends in “-ski”

Semi-Markov models for IE

• Train on sequences of labeled segments, not labeled words.S=(start,end,label)

• Build probability model of segment sequences, not word sequences

• Define features f of segments

• (Approximately) optimize feature weights on training data

f(S) = words xt...xu, length, previous words, case information, ..., distance to known name

maximize:

m

iii

1

)|Pr(log xS

with Sunita Sarawagi, IIT Bombay

Details: Semi-Markov model

Details: Semi-Markov model

Conditional Semi-Markov models

CMM:

CSMM:

A training algorithm for CSMM’s (1)

Review: Collins’ perceptron training algorithm

Correct tags

Viterbi tags

A training algorithm for CSMM’s (2)

Variant of Collins’ perceptron training algorithm:

voted perceptron learner for TTRANS

like Viterbi

A training algorithm for CSMM’s (3)

Variant of Collins’ perceptron training algorithm:

voted perceptron learner for TTRANS

like Viterbi

A training algorithm for CSMM’s (3)

Variant of Collins’ perceptron training algorithm:

voted perceptron learner for TSEGTRANS

like Viterbi

Sample CSMM features

Experimental results

• Baseline algorithms:– HMM-VP/1: tags are “in entity”, “other”– HMM-VP/4: tags are “begin entity”, “end entity”,

“continue entity”, “unique”, “other”– SMM-VP: all features f(w) have versions for “f(w) true for

some w in segment that is first (last, any) word of segment”– dictionaries: like Borthwick

• HMM-VP/1: fD(w)=“word w is in D”• HMM-VP/4: fD,begin(w)=“word w begins entity in D”,

etc, etc• Dictionary lookup

Datasets used

Used small training sets (10% of available) in experiments.

Results

Results: varying history

Results: changing the dictionary

Results: vs CRF

Results: vs CRF