Meltwater Budapest, April 2016

The importance of entities

Babak Rasolzadeh, Director of Data Science Research

1. Company background2. Data Science @ Meltwater3. Challenges with NLP at Large scale4. Entities, entities, entities

a. Social NERb. ELSc. Knowledge Graph

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What is Meltwater?

● A business intelligence company → Providing insights from data outside the firewall (news, blogs, social media, etc.)

● Born in Oslo, in 2001.● Founder and CEO: Jorn Lyssegen● www.meltwater.com

● 30K+ clients all over the World.● 1000+ employees● 60+ offices around the world, mostly sale.● Tech offices: USA, Germany, Sweden, Hungary, India.

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Why?

own brand

competitors

leads

partners product reviews

own industry

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What?

Uses Meltwater to find out about new instances of vandalism and break-ins. Often, the victim is in need of services

Uses Meltwater to help determine how public perception of certain

ingredient chemicals will influence adoption & sales

Uses Meltwater to be alerted of when certain patent will expire in

target markets

Uses Meltwater to monitor the performance and popularity of news

anchors and programs

Uses Meltwater social listening to estimate and prevent

infrastructure attacks

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How?

UnstructuredDocument Stream

Pipeline

Enrichments

Search/Storage

Enriched Documents

High PerformanceIndexes

ProcessingServices

API Layer

APPSBackup Storage

Raw Documents

15 supported languages in pipeline(EN, DE, SV, NO, FI, ZH, JP, FR, ES, DA, NL, PT, AR, IT, HI)

Typical enrichments○ Sentiment analysis○ Thematic analysis○ Categorization○ Keyphrase extraction○ Named Entity Recognition○ Named Entity Disambiguation

NLP & Data Science at Meltwater

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What other than NLP?

● Recommendation Engines

DOC3DOC3

DOC3

DOC3DOC3

DOC8

Realtime recommenderengine

● Correlation and predictive pattern recognition

● Word2vec techniques

concept 3concept 1

concept 2

“British American Tobacco" or "British American Tobbaco" or (BAT near tobacco) or "英美煙草" or (("Lucky Strike" or "Dunhill" or "Pall Mall") near/15 cigarette*)

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Machine Learning Terminology

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Challenges with Data Science (NLP) at scale

• High DPS (~2000) and a lot to do! (tokenization, lemmatization, stemming, POS tagging, categorization, sentiment, NER, ...) with racing conditions!

Pipeline

Enrichments

SV

EN

DE

POS NER• Training data labelling is costly! x15• Contextual information expensive (computationally).• Noise, missing data, variation (e.g. slang), data types, ...

Knowledge Base StrategyEntities, entities, entities

don - July 2015

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Knowledge Base StrategyWhat are Named Entities (NE)?

● Non-linguistic definition○ Referable entities○ Usually Proper Names○ Single or multi-word

→ I know this man. He might be Charles.→ He lives in Stockholm. He is Swedish.

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Knowledge Base StrategyWhat is Named Entity Recognition (NER)?

1. Extracting NEs from a text.2. Categorizing NEs from a set of predefined categories.

John lives in Stockholm. He works at Ericsson.

Categories of {PER, LOC, ORG, MISC, PROD}

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Knowledge Base StrategyWhat NER is not?

● NER is not event recognition. ● NER recognises entities in text, and classifies them in some way, but it

does not create templates, nor does it perform co-reference or entity linking.

● NER is not just matching text strings with pre-defined lists of names. It only recognises entities which are being used as entities in a given context. (i.e. not easy!)

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● Key part of Information Extraction system● Robust handling of proper names essential for many applications● Pre-processing for different classification levels● Information filtering● Information linking● Entity level sentiment● Knowledge graph

Why NER?

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Knowledge Base StrategyWhy NER?

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Knowledge Base StrategyWhy NER?

Pepsi spooks Coke with this Halloween themed ad.

Entity specific sentiment analysis a.k.a ELS

Knowledge Base StrategySo what about Social…?

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Supervised Learning

❏ Hidden Markov Model (HMM) Freitag and Mccallum, 1999; Leek, 1997.

❏ Conditional Markov Model (CMM) Borthwick, 1999; McCallum et al., 2000.

✓ Conditional Random Field (CRF) Lafferty, 2001; Ratinov and Roth, 2009.

How to do NER? (state-of-the-art)

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● Ground truth data collection for NER is very expensive● Solutions:

○ Automatic NER annotation using Wikipedia data○ Applying Latent Dirichlet Analysis (LDA) based NER detection

using Gazetteer data.

Training data

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NER pipeline

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Extensive lists of names for a specific category● PER

○ First names (male-female) and surnames, their frequency● LOC

○ Cities, Countries○ Population

● ORG○ Name of companies from Yellow pages.

Gazetteers help

Disadvantages○ Difficult to create and maintain (or expensive if commercial)○ Usefulness varies depending on category ○ Ambiguity○ Words occur in more lists of different types (PER, LOC, FAC,...)

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Let’s say we want to estimate the likelihood of the bi-gram "to Shanghai", without having seen this in a training set.

The system can obtain a good estimate if it can cluster "Shanghai" with other city names (like “London”, “Beijing”), then make its estimate based on the likelihood of phrases such as "to London", "to Beijing" and "to Denver"

Brown clustering - motivation

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● Proposed by Brown et al. (1992) (a.k.a “IBM clustering”)● Hierarchical class-based labeling method.● Bottom-up● Unsupervised learning

○ Doesn't need labeled data but rather large set of raw text.● Greedy technique to maximize bi-gram MI.● Merge words by contextual similarity.

Brown clustering (1)

( )

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Brown clustering (2)

● Large amount of data○ Similar words appear in similar contexts.○ More precisely: similar words have similar distribution of words to their

immediate left and right.● Example: “the” and “a” both are determinant.

○ Frequency of immediate words on their left and right:

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Brown clustering (3)

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Hmm...easy?

● What are the challenges in real applications?● What about moving to other languages?● What about moving to social domain?

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Disambiguation

What is the entity category of “Washington”?

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Different languages

● Tokenization○ Chinese & Japanese: Words not separated

● Part of speech○ Nouns

■ English: only number inflection■ German: number, gender and case inflection

○ Verbs■ English: regular verb 4, irregular verb up to 8 distinct forms■ Finnish: more than 10,000 forms

● NER: Shape feature○ English: Only proper nouns capitalized○ German: All nouns capitalized

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Different languages

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Different languages

Studying of linguistic properties of a language is important!

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Editorial vs. Social

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Challenges in Social NER

● The performance of “off-the-shelf” NER methods degrades severely when applied on Twitter data

● Tweets○ are short: 140 character limit.○ cover wide range of topics.○ are written grammatically in broken language. ○ are written fast and posted from anywhere: a lot of mis-spelling.

→ a solution which considers social characteristics of text

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Challenges in Social NERExamples of noisy data● Jaguar's gonna like this episode of #MadMen even less than last week's, I bet.● Dane Bowers is in Asda I cant believe.it luckiest girl in the world omf i cant believe

it omg● A feel good story RT @DailyBreezeNews: Santa Claus arrives by helicopter at LAX

to greet local school

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Solution (1)Adapting existing features to social properties(POS tagger of editorial NER performs really poor

when it comes to social documents.)

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Solution (2)

Weight (importance) of each CRF feature

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Results

● Training Data○ ~76K tweets labeled by human

annotator.

● Inter agreement of two annotators.

● Test Data○ ~9.1K tweets labeled by human

annotator.

● Improvement compared state-of-the-art methodRitter, A. et al. Named entity recognition in tweets: An experimental study. EMNLP ’11, pages 1524–1534.

Knowledge Base StrategyWhat about sentiment….?

Document Level Sentiment - how it works

Inter-annotator agreement ~80%*

* http://bit.ly/human-sentiment

Document Level Sentiment - how it works

Machine Learning MagicSupervised learningNaive bayes - BernoulliNB, GaussianNB, MultinomialNBSupport Vector Machines - LinearSVM, RbfSVMMaximum Entropy Model - GIS, IIS, MEGAM, TADMMLP - RecurrentNN

Document Level Sentiment - how it works

Machine Learning Magic

Document Level Sentiment - current status

~60-70% (depending on language)

Not too terrible, considering that human performance is at best ~80%...

...but why is it so hard?

Document Level Sentiment - how it’s used

Document Level Sentiment - how it’s used

Document Level Sentiment - the problem

Document Level Sentiment - the problem

Negative

Neutral

Document Level Sentiment - the problem

“Those numbers underline a growing gap between McDonald's and today's fast-food customers. It will only get wider with another year's worth of the same uninspired fare that has made McDonald's customers easy pickings for Panera Bread, Chick-fil-A, Chipotle Mexican Grill and others.

”

Negative

Positive

Does not make sense for our industry!

Knowledge Base StrategyEntity Level Sentiment (ELS)

Entity Level Sentiment - motivation

● DLS imprecise and wrong for our customers● Entities are of main importance for our customers ● We already have NER (Named Entity Recognition) technology

Idea:

Identify the sentiment towards each particular entity in a text!

Entity Level Sentiment - how it works

NER

BMW: PositiveMercedes: NeutralToyota: Negative…

Entity Level Sentiment - how it works

Entity1: PositiveEntity2: NeutralEntity3: Negative…

E1:PositiveE2: NeutralE3: Negative

E1:PositiveE2: NeutralE3: Negative

E1:PositiveE2: NeutralE3: Negative

Entity Level Sentiment - how it works

Entity1: PositiveEntity2: NeutralEntity3: Negative…

NER

Entity Level Sentiment - use case

Entity Level Sentiment - current status

● ELS is considered a very tough problem in NLP/ML● The accuracy of state-of-the-art ELS is currently very low

(~45%)

Knowledge Base StrategyThe holy grail : The Graph Knowledge Base

don - July 2015

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Entities + Relationships

As the types of entities and their relationships grows so does the capacity to infer insights that depend on connectivity and eventually one can answer questions thatwould otherwise not bepossible with only separate datasets!

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KB ArchitectureUnstructuredDocument Stream

Pipeline

Enrichments

Graph Search

Enriched Documents

High PerformanceIndexes

ProcessingServices

API Layer

KnowledgeBase (Graph)

I/O

External Data Providers

Updates/subscriptions

Lookups

APPSBackup Storage

Raw Documents

Knowledge Base StrategyWhy is it hard?

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Composing the KB

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Data Acquisition trade-offs

High volume

High quality

Chea

pManual data acquisition

Special crawlers,Smart algorithms

Acquisitions, partnerships

low quality

expensivelow

volume

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Composing the KB - Scalability

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Scalability Requirements - next stepsCompanies ~ 100 million worldwidePeople ~ 500 million (including media influencers)Products ~ 500 million

~1 billion entities all the connections between them

→ billions of nodes, trillions of edges!

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Composing the KB - New features

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Improve entity search - company NED

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Improve entity search - person NED

Robert Gates22nd Secretary of Defense

William Henry Gates IIIformer CEO & cofounder of Microsoft

“Who is Mr. Gates?”

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Emerging competition

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Map influencer network

influencer score ~ eg. PageRank

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Suggested read

● Ratinov 2009 (challenges in NER): paper.● ArkCMU (social): paper, code.● Ritter et al (social): paper, code.● Stanford NLP NER (editorial): paper, code. ● Brown clustering

○ brown clustering: video ○ Word Representations and N-grams: video

● Transforming Wikipedia into Named Entity Training Data: paper.