Cognitive Computation Group

Resources for Semantic Similarity

http://cogcomp.cs.illinois.edu

Textual Inference

Given a task like Question Answering… …you have a large set of documents, e.g. all articles from the

New York times for 2011 and 2012 …and a set of questions, e.g. “Who participated in the

gubernatorial debates in January 2012?” …You must return excerpts of the documents that answer the

questions.

What are the challenges?

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QA Example

Consider the following example question, and a sample document excerpt that might answer it:

Q. Where is the headquarters of the parent company of Solahart Services?

A. Aztec Solar, Inc. recently acquired Solahart Services of Stockton California. Aztec Solar, a Sacramento based residential and commercial solar company, is excited about acquiring Solahart's regional and national solar customers.

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QA Example

Given the QA pair on the previous page, a human reader might make the following inference steps:

1.Aztec Solar, Inc. recently acquired Solahart Services Aztec Solar, Inc. is the parent company of Solahart Services.

2.“Aztec Solar, Inc.” looks like a company name

3.Aztec Solar, a Sacramento based residential and commercial solar company Aztec Solar is based in Sacramento

4.“Aztec Solar” == “Aztec Solar, Inc.”

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QA Example An automated system may use a matching process like

this:

1. Rewrite the question:The headquarters of the parent company of

Solahart Services is in <LOCATION>

2. Match question entities and tokensLOCATION Sacramento; company Aztec Solar, Inc.

3. Apply structure-mapping rules<LOCATION>-based <COMPANY>

<COMPANY> headquarters in <LOCATION>

4. This example can be easily perturbed to be more difficult (to thwart a shallow system)

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Outline Introduction: Textual Inference example Semantic Textual Similarity task LLM: a baseline system Comparators

Overview Instances: WNSim, NESim

Annotators POS, Chunk, NER, Coreference, SRL

Curator Edison

Data structures Calling Curator Feature Extraction

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Textual Inference: Semantic Similarity

Grand NLP challenge: work at level of meaning of text Do these two sentences mean the same thing?

1. John said he is considered a witness but not a suspect.

2. "He is not a suspect anymore," John said.

If they are different, how different are they? …rate similarity on a scale 0…5: 0 == different topic; 5 ==

paraphrase http://www.cs.york.ac.uk/semeval-2012/task6/data/uploads/

datasets/train-readme.txt

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Examples from STS training corpus

Nationally, the federal Centers for Disease Control and Prevention recorded 4,156 cases of West Nile, including 284 deaths.

There were 293 human cases of West Nile in Indiana in 2002, including 11 deaths statewide.

Score: 1.667

Chavez said investigators feel confident they've got "at least one of the fires resolved in that regard."

Albuquerque Mayor Martin Chavez said investigators felt confident that with the arrests they had "at least one of the fires resolved.“

Score: 3.800

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CANDIDATE BASELINE:

LEXICAL LEVEL MATCHING (LLM)

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Words MatterApproximate similarity of meaning via lexical overlap – how many

words in common

But this isn’t exactly fool-proof…

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John Smith bought three cakes and two oranges

John bought two oranges

John Smith bought three cakes and two oranges

John bought three oranges

LLM Scoring Designed for Textual Entailment (inherently asymmetric) Proportion of matched Hypothesis tokens, normalized by

length of shorter text Let T be the Text, containing tokens indexed by j Let H be the Hypothesis, with tokens indexed by i Let S(word1, word2) be a lexical similarity function that

returns a value in the range [0,1]

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||

),(max

H

thS

Score iji

jLLM

LLM code

http://cogcomp.cs.illinois.edu/page/software_view/LLM

import edu.illinois.cs.cogcomp.mrcs.comparators.LlmComparator;

String source = "Of the three kings referred to by their last names, Atawanaba was the oldest.";

String target = "Three kings were named in the lawsuit.";

LlmComparator llm = new LlmComparator( config );

double result = llm.compareStrings( source, target );

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Can we do better? Depends on the application… more advanced task may

require more sophisticated patterns to separate classes Sparsity of features

Many words/sequences of words may not occur very often This means a learned classifier may not generalize well More abstract representation can help

Ambiguity of words – e.g. “terminal”, “moving” Additional information may help

Meaning encoded in structure – e.g. “Matthew Smith, the Maverick’s last hope…”

NLP annotation tools generally abstract over underlying words so that features generalize better

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COMPARATORS

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So you want to compare some text…. How similar are two words? Two strings? Two

paragraphs? Depends on what they are String edit distance is usually a weak measure … think about coreference resolution…

Solution: specialized metrics

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String 1 String 2 Norm. edit sim.

Shiite Shi’ ‘ite 0.667

Mr. Smith Mrs. Smith 0.900

Wilbur T. Gobsmack Mr. Gobsmack 0.611

Frigid Cold 0.167

Wealth Wreath 0.667

Paris France 0.167

WNSim Generate table mapping terms linked in WordNet

ontology Synonymy, Hypernymy, Meronymy

Score reflects distance (up to 3 edges, undirected – e.g. via lowest common subsumer)

Score is symmetric

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String 1 String 2 WNSim similarity

Shiite Shi’ ‘ite 0

Mr. Smith Mrs. Smith 0

Wilbur T. Gobsmack Mr. Gobsmack 0

Frigid Cold 1

Wealth Wreath 0

Paris France 0

Using WNSim Install and run the WNSim code

http://cogcomp.cs.illinois.edu/page/software_view/WNSim Sets up an xmlrpc server Expects xmlrpc ‘struct’ data structure (analogous to Dictionary)

STRUCT { FIRST_STRING: aString; SECOND_STRING:

anotherString }

Returns another xmlrpc data structure:

STRUCT { SCORE: aDouble; REASON: aString }

USE: call and cache (reduce network latency overhead) NOTE: LLM code has Java client…

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WNSim via Metric interface

String metricHost = “…”;

int metricPort = …;

XmlRpcMetricClient client = new XmlRpcMetricClient( “WNSim”,

metricHost,

metricPort

);

MetricResponse response = client.compareStrings( source_, target_ );

double score = response.score;

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NESim Set of entity-type-specific measures

Acronyms, Prefix/Title rules, distance metric

Score reflects similarity based on type information Score is asymmetric

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String 1 String 2 Norm. edit distance

Shiite Shi’ ‘ite 0.922

Joan Smith John Smith 0

Wilbur T. Gobsmack Mr. Gobsmack 0.95

Frigid Cold 0

Wealth Wreath 0.900

Paris France 0.411

Using NESim NESim package from CCG web site

http://cogcomp.cs.illinois.edu/page/software_view/NESim

NESim can use context to help determine similarity Specify token offsets of NE string to indicate context (optional) Specify Type as one of PER, LOC, ORG (optional)

[<Type>#]<original string>[#<start offset>#<end offset>]

Note: offsets are inclusive, token-based, zero offset

Uses specialized resources depending on the type (if specified) Rules/gazetteers for People’s names Acronyms for Organizations

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Using NESim (cont’d) Returns a score in [0, 1]

Threshold of 0.8 or higher is advised Weakly similar names are generally not semantically close

Put jar on classpath, call programmatically Loads large lists, so instantiate once only

import edu.illinois.cs.cogcomp.entityComparison.core.EntityComparison;

EntityComparison entityComparator = new EntityComparison();

entityComparator.compare( aName, anotherName );

double currentScore = entityComparator.getScore();

Problem: identifying NE boundaries, types

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ANNOTATORS

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Available from CCG

Tokenization/Sentence Splitting Part Of Speech Chunking Named Entity Recognition Coreference Semantic Role Labeling

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Tokenization and Sentence Segmentation Given a document, find the sentence and token

boundaries

The police chased Mr. Smith of Pink Forest, Fla. all the way to Bethesda, where he lived. Smith had escaped after a shoot-out at his workplace, Machinery Inc.

Why? Word counts may be important features Words may themselves be the object you want to classify “lived.” and “lived” should give the same information different analyses need to align if you want to leverage multiple

annotators from different sources/tasks

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Tokenization and Sentence Segmentation ctd.

Believe it or not, this is an open problem No agreed standard for token-level segmentation

e.g. “American-led” vs. “American - led”? e.g. “$ 32 M” vs “$32 M” and “$32M”?

Different tasks may use different standards No wildly successful sentence segmenter exists (see the

excerpts in news aggregators for some nice errors) Noisier text (e.g. online consumer reviews) poorer

performance (for reasons like inconsistent capitalization) LBJ distribution includes the Illinois tokenizer and

sentence segmenter

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Part of Speech (POS) Allows simple abstraction for pattern detection

Disambiguate a target, e.g. “make (a cake)” vs. “make (of car)”

Specify more abstract patterns, e.g. Noun Phrase: ( DT JJ* NN )

Specify context in abstract way e.g. “DT boy VBX” for “actions boys do” This expression will catch “a boy cried”, “some boy ran”, …

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POS DT NN VBD PP DT JJ NN

Word The boy stood on the burning deck

POS DT NN VBD PP DT JJ NN

Word A boy rode on a red bicycle

Chunking Identifies phrase-level constituents in sentences

[NP Boris]  [ADVP regretfully]  [VP told]  [NP his wife]  [SBAR that]  [NP their child]  [VP could not attend] [NP night school]  [PP without]  [NP permission] .

Useful for filtering: identify e.g. only noun phrases, or only verb phrases Groups modifiers with heads Useful for e.g. Mention Detection

Used as source of features, e.g. distance (abstracts away determiners, adjectives, for example), sequence,… More efficient to compute than full syntactic parse Applications in e.g. Information Extraction – getting (simple)

information about concepts of interest from text documents

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Named Entity Recognition Identifies and classifies strings of characters

representing proper nouns

[PER Neil A. Armstrong] , the 38-year-old civilian commander, radioed to earth and the mission control room here: “[LOC Houston] , [ORG Tranquility] Base  here; the Eagle has landed."

Useful for filtering documents “I need to find news articles about organizations in which Bill

Gates might be involved…” Disambiguate tokens: “Chicago” (team) vs. “Chicago” (city)

Source of abstract features E.g. “Verbs that appear with entities that are Organizations” E.g. “Documents that have a high proportion of Organizations”

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Coreference Identify all phrases that refer to each entity of interest –

i.e., group mentions of concepts

[Neil A. Armstrong] , [the 38-year-old civilian commander], radioed to [earth]. [He] said the famous words, “[the Eagle] has landed”."

The Named Entity recognizer only gets us part-way… …if we ask, “what actions did Neil Armstrong perform?”,

we will miss many instances (e.g. “He said…”) Coreference resolver abstracts over different ways of

referring to the same person Useful in feature extraction, information extraction

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Semantic Role Labeler

SRL reveals relations and arguments in the sentence (where relations are expressed as verbs)

Cannot abstract over variability of expressing the relations – e.g. kill vs. murder vs. slay…

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CURATOR

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Big NLP We introduced a lot of tools, some of them quite

sophisticated The more complex, the bigger the memory requirement

NER: 1G; Coref: 1G; SRL: 4G ….

If you use tools from different sources, they may be… In different languages Using different data structures

If you run a lot of experiments on a single corpus, it would be nice to cache the results …and for your colleagues, nice if they can access that cache.

Curator is our solution to these problems.

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Curator Supports distributed NLP resources

Central point of contact Single set of interfaces Code generation in many languages (using Thrift)

Programmatic interface Defines set of common data structures used for interaction

Caches processed data Enables highly configurable NLP pipeline

Overhead: Annotation is all at the level of character offsets:

Normalization/mapping to token level required Need to wrap tools to provide requisite data structures

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Curator

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NER

SRL

POS, Chunker

Cache

Curator

Using Curator for Flexible NLP Pipeline

http://cogcomp.cs.illinois.edu/curator/demo/index_beta.html

For this class only: dedicated curator instance Temporary instance with host, port accessible to class members

http://cogcomp.cs.illinois.edu/trac/wiki/CuratorDataStructures

Recommended: access using Edison library (next)

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Edison: An NLP Library

http://cogcomp.cs.illinois.edu/software/edison/ Convenient interface to Curator

Converts to token-level indexing (often more convenient)

Supports feature extraction over trees Apply to syntactic parse/dependency, and to SRL/NOM E.g. see

http://cogcomp.cs.illinois.edu/software/edison/FeaturesExample.html for examples of dependency path features

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Serializing TextAnnotationspublic void serializeAnnotations( List< TextAnnotation > annotations_, String

outputFile_ ) throws Exception {        try {            ObjectOutputStream objOut =

new ObjectOutputStream(new FileOutputStream( outputFile_ ) );                     objOut.writeObject( new Integer( annotations_.size() ) );                        for ( TextAnnotation ta: annotations_ ) {                System.err.println( "serializing TA for text '" + ta.getText() + "'..." );                objOut.writeObject( ta );            }            objOut.close();        } catch (IOException e) {

…

        }        return;    } 

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K-best Views in Curator

The Charniak and Stanford parsers can be run in K-best mode

These will be added to Curator with k=50 This will be quite disk-hungry These components will probably *not* be cached

Curator uses a MultiParser interface for k-best parsers Generates a parse view in Record The parse view is a List of Forests: the k-th Forest contains the

k-th best parse for all sentences in record

Edison does NOT yet directly support getting k-best parses from Curator, BUT…

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K-best views in Edison

Edison supports k-best views

List<View> topKParses  = ...; // A list of top-k parses, say from Charniak

ta.addView(ViewNames.PARSE_CHARNIAK, topKParses);

List<View> parses = ta.getTopKViews(ViewNames.PARSE_CHARNIAK);

int tokenId = 17; // some token

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Edison k-best example cont’d

Constituent c = new Constituent("", "",ta, tokenId, tokenId+1); 

int treeId =0 ;

for(View parseTree: parses) {

    for(Constituent parseConstituent: parseTree.

    where(Queries.containsConstituent(c))) {

// do something with parseConstituent belonging to tree "treeId"

    }

    treeId++;

}

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A FINAL WORD

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LLM and Semantic Similarity LLM was designed for Textual Entailment, and is

asymmetric by design This task is a little different – trying to assess level of

semantic equivalence of two sentences S1 and S2 Still want to normalize (don’t want all short sentence

pairs to have lower scores than long sentence pairs), but consider evaluating for both (S1, S2) and for (S2, S1)

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FIN

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