Recommended
Recommended
More Related Content
Viewers also liked
Viewers also liked (11)
Similar to Babak Rasolzadeh: The importance of entities
Similar to Babak Rasolzadeh: The importance of entities (20)
More from Zoltan Varju
More from Zoltan Varju (20)
Recently uploaded
Recently uploaded (20)
Babak Rasolzadeh: The importance of entities
- 1. Meltwater Budapest, April 2016 The importance of entities Babak Rasolzadeh, Director of Data Science Research
- 2. 1. Company background 2. Data Science @ Meltwater 3. Challenges with NLP at Large scale 4. Entities, entities, entities a. Social NER b. ELS c. Knowledge Graph
- 3. 3 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.
- 4. 4 Why? own brand competitors leads partners product reviews own industry
- 5. 5 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
- 6. 6 How?
- 7. Unstructured Document Stream Pipeline Enrichments Search /Storage Enriched Documents High Performance Indexes Processing Services 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
- 8. 8 What other than NLP? ● Recommendation Engines DOC3 DOC3 DOC3 DOC3 DOC3 DOC8 Realtime recommender engine ● Correlation and predictive pattern recognition ● Word2vec techniques concept 3 concept 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*)
- 10. 10 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, ...
- 11. Knowledge Base Strategy Entities, entities, entities don - July 2015
- 12. 12 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.
- 13. 13 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}
- 14. 14 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!)
- 15. 15 ● 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?
- 16. 16 Knowledge Base StrategyWhy NER?
- 17. 17 Knowledge Base StrategyWhy NER? Pepsi spooks Coke with this Halloween themed ad. Entity specific sentiment analysis a.k.a ELS
- 18. Knowledge Base Strategy So what about Social…?
- 19. 19 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)
- 20. 20 ● 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
- 21. 21 NER pipeline
- 22. 22 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,...)
- 23. 23 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
- 24. 24 ● 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) ( )
- 25. 25 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:
- 27. 27 Hmm...easy? ● What are the challenges in real applications? ● What about moving to other languages? ● What about moving to social domain?
- 28. 28 Disambiguation What is the entity category of “Washington”?
- 29. 29 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
- 31. 31 Different languages Studying of linguistic properties of a language is important!
- 33. 33 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
- 34. 34 Challenges in Social NER Examples 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
- 35. 35 Solution (1) Adapting existing features to social properties (POS tagger of editorial NER performs really poor when it comes to social documents.)
- 36. 36 Solution (2) Weight (importance) of each CRF feature
- 37. 37 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 method Ritter, A. et al. Named entity recognition in tweets: An experimental study. EMNLP ’11, pages 1524–1534.
- 38. Knowledge Base Strategy What about sentiment….?
- 39. Document Level Sentiment - how it works Inter-annotator agreement ~80%* * http://bit.ly/human-sentiment
- 40. Document Level Sentiment - how it works Machine Learning Magic Supervised learning Naive bayes - BernoulliNB, GaussianNB, MultinomialNB Support Vector Machines - LinearSVM, RbfSVM Maximum Entropy Model - GIS, IIS, MEGAM, TADM MLP - RecurrentNN
- 41. Document Level Sentiment - how it works Machine Learning Magic
- 42. 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?
- 43. Document Level Sentiment - how it’s used
- 44. Document Level Sentiment - how it’s used
- 45. Document Level Sentiment - the problem
- 46. Document Level Sentiment - the problem Negative Neutral
- 47. 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!
- 48. Knowledge Base Strategy Entity Level Sentiment (ELS)
- 49. 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!
- 50. Entity Level Sentiment - how it works NER BMW: Positive Mercedes: Neutral Toyota: Negative …
- 51. Entity Level Sentiment - how it works Entity1: Positive Entity2: Neutral Entity3: Negative … E1:Positive E2: Neutral E3: Negative E1:Positive E2: Neutral E3: Negative E1:Positive E2: Neutral E3: Negative
- 52. Entity Level Sentiment - how it works Entity1: Positive Entity2: Neutral Entity3: Negative … NER
- 53. Entity Level Sentiment - use case
- 54. 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%)
- 55. Knowledge Base Strategy The holy grail : The Graph Knowledge Base don - July 2015
- 56. 56 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 that would otherwise not be possible with only separate datasets!
- 57. 57 KB Architecture Unstructured Document Stream Pipeline Enrichments Graph Search Enriched Documents High Performance Indexes Processing Services API Layer Knowledge Base (Graph) I/O External Data Providers Updates/subscriptions Lookups APPSBackup Storage Raw Documents
- 58. Knowledge Base Strategy Why is it hard?
- 60. 60 Data Acquisition trade-offs Highvolume High quality Cheap Manual data acquisition Special crawlers, Smart algorithms Acquisitions, partnerships low quality expensive low volume
- 61. 61 Composing the KB - Scalability
- 62. 62 Scalability Requirements - next steps Companies ~ 100 million worldwide People ~ 500 million (including media influencers) Products ~ 500 million ~1 billion entities all the connections between them → billions of nodes, trillions of edges!
- 63. 63 Composing the KB - New features
- 64. 64 Improve entity search - company NED
- 65. 65 Improve entity search - person NED Robert Gates 22nd Secretary of Defense William Henry Gates III former CEO & cofounder of Microsoft “Who is Mr. Gates?”
- 67. 67 Map influencer network influencer score ~ eg. PageRank
- 68. 68 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.