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KDIR2015-Entity Linking and Knowledge Discovery in Microblogs-Presentation
- 1. PIKAKSHI MANCHANDA DISCo, Universityof Milano-Bicocca, Milan, Italy @pikakshi787 Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 KDIR 2015, Lisbon,12th November, 2015
- 2. People communicateand share information increasingly through social media platforms Fresh information emerging in real-time on social media platforms primarily New entities (newly emerging, newly relevant/popular) New relationships Factual information Events 2 SOCIAL MEDIA: ENTITIES-EMOJIS-EVENTS Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 3. WHY INFORMATION EXTRACTION?? 3 Existing entities Newentity (Product Launch) Apple Watch Product IBM OS2 Product Apple Company New Relations WHY SOCIAL MEDIA PLATFORMS?? Fresh Real-time info Incomplete KBs Unstructured Web Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 4. MOTIVATION 4 Manchanda et al.,Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 Bridging the gap between Unstructured Web and Web of Data • Intrinsic incompleteness in KBs Information Extraction from social media streams (microposts,..) • Named Entity Recognition (NER) • Named Entity Classification • Named Entity Linking (NEL) Knowledge Base (KB) enrichment • Identify new knowledge • Improve NER • Lexically enriching knowledge bases for existing & new entities
- 5. INFORMATION EXTRACTION NamedEntity Recognition: Task of identifying named entities in a piece of text Named Entities: text fragments that refer to entities in the real world (proper nouns..) Named Entity Classification: Classifying recognized named entities into entity types such as PERSON, LOCATION, ORGANIZATION… Named Entity Linking: Linking the identified named entities to resources in a knowledge base (such as Wikipedia, DBpedia) 5 Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 6. 6 The Town mightbe one of the best movies I have seen all year. So, so good. And don't worry Ben, we already forgave you for Gigli. Really. http://dbpedia.org/page/Ben_Affleck foaf:Person yago:AmericanFilmActors http://dbpedia.org/page/Gigli dbo:Film yago:AmericanFilms http://es.dbpedia.org/page/The_Town dbpedia-owl:Film schema.org/Movie Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 NamedEntityLinking INFORMATION EXTRACTION
- 7. 7 The Town mightbe one of the best movies I have seen all year. So, so good. And don't worry Ben, we already forgave you for Gigli. Really. http://dbpedia.org/page/Ben_Affleck foaf:Person yago:AmericanFilmActors http://dbpedia.org/page/Gigli dbo:Film yago:AmericanFilms http://live.dbpedia.org/page/The_Town_(2012_TV_series) dbo:TelevisionShow http://schema.org/CreativeWork Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 INFORMATION EXTRACTION NamedEntityLinking
- 8. Entity Recognition andLinking in microposts has been reported to be quite challenging: 1. Short and noisy nature, typographic errors, shortening of words, ambiguity, polysemy (Liu et al. 2013, Ritter et al. 2011, Meij et al. 2012) 2. Out Of Vocabulary (OOV) entity mention identification problem The Big Bang Theory being referred as TBBT 3. Out of Knowledge base (OOKB) entity problem A new upcoming company Widro 8 CHALLENGES Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 9. 9 Systems/Tools Approach DomainEntity Types/Classes Taxonomy ANNIE Gazetteers & FSM Newswire 7 (adapted) MUC Stanford NER CRF Newswire 4, 3 or 7 CoNLL, ACE Alchemy API Machine Learning Unspecified 324 Alchemy NERD-ML KNN & Naïve Bayes Twitter 4 NERD TextRazor Machine Learning Unspecified 1779 DBpedia, Freebase Ritter et al., 2011 CRF Twitter 3 or 10 CoNLL, ACE Liu et al. 2011 KNN & CRF Twitter 4 CoNLL, ACE Kalina et al, 2013 Gazetteers & FSM Twitter 3 or 10 CoNLL Derczynski et al, 2015 Structured Learning (CRF) Twitter 10 Freebase ENTITY RECOGNITION Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 10. 10 Tools Taxonomy Approach/Features used Domain DBpedia Spotlight (Mendes et al., 2011) DBpedia, Freebase, Schema.org Gazetteers and Similarity Metrics Unspecified TAGME (Ferragina and Scaiella, 2010) Wikipedia Wikipedia anchor texts and the pages linked to those anchor texts Short texts YODIE (Damljanovic and Bontcheva, 2012) DBpedia Similarity metrics and URI frequency Twitter Babelfy (Moro et al., 2014) BabelNet semantic network Graph-based approach, semantic signatures Short text Meij et al., 2012 Wikipedia n-gram features, concept features, and tweet features Twitter S-MART, Yang et al, 2015 Wikipedia Structural Learning (Tree-based) Twitter Weasel (Tristram et al, 2015) DBpedia Machine Learning (using SVM) Newspaper Articles Guo et al., 2013 Wikipedia Structural SVM Twitter Yamada et al., 2015 Wikipedia Supervised (String matching, n-grams) Twitter Mention detection & disambiguation system: Pipeline Use NEL to learn how to perform NER: pipeline Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 ENTITY LINKING
- 11. THE PROPOSED SYSTEM An end-to-end IE framework for microblogs to orchestrate NER and NEL • Entity Recognition and Classification • Candidate match retrieval for identified entities • Entity linking • Leverage entity linking to improve named entity classification Gold-standard corpus of ~2400 tweets (Ritter et al., 2011) Ground Truth: Manually curated set of 1616 named entities identified with entity types Use of DBpedia as an external KB 11 Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 12. 12 FRAMEWORK Manchanda et al.,Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 Named Entity Recognition Tweet Surface forms of named entities Index (rdfs:label) Entity Search Top-k labels for each surface form Resource description f Surface form, entity type & context KB Entity Disambiguation Resource foreach label Entity Linking Improvement of NER Resource for surface form
- 13. 13 ENTITY RECOGNITION Manchanda etal., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 Named Entity Recognition Tweet Surface forms of named entities Index (rdfs:label) Entity Search Top-k labels for each surface form Resource description f Surface form, entity type & context KB Entity Disambiguation Resource foreach label Entity Linking Improvement of NER Resource for surface form
- 14. T-NER groundedon Conditional Random Fields (Sutton and McCallum, 2006) Classifying each entity e into one or more entity type/class c with a probability score PCRF(e,c) Experimental Analysis: Entity Recognition NER Systems: T-NER (Ritter et al. 2011) 14 Entity type: O Entity type: Geo-Loc Entity type: Band Entity type: Sportsteam Identification Errors “@vogueglamGIRL Ah I know! She is simply the best in The Sept Issue. My boyfriend’s aunt worked for Anna Wintor in NY” Classification Errors “Cant wait for the ravens game tomorrow....go ray rice!!!!!!!” Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 PCRF (e, c) = exp (Σ wkfk (e, c)) k=1 K
- 15. Text Phrase Classification Level Example Classification (%) Entity EntityType Entities (1496) Correctly Classified Justin Bieber Person 61.57 Incorrectly Classified Chicago Person 37.96 Segmentation Error Alpha, Omega (Alpha-Omega) Geo-Location, Band 0.47 Non- Entities (44k) Correctly Classified It Outside (O) 99.8 Incorrectly Classified justthen Person 0.2 T-NER Classification Performance 15 Identifies 1496 named entities from the GS, in contrast to 1616 entities in ground truth. 8% of entities are not even recognized and thus classified as non-entities (amongst other 44k tokens) Entity Type Error (%) Band 73.83 Company 21.9 Facility 54.79 Geo-Location 19.75 Movie 75.83 Other 46.29 Person 28.18 Product 39.70 Sportsteam 48.27 TVshow 48.71 Classification Error Rate: T-NER Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 16. 16 ENTITY LINKING Manchanda etal., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 Surface forms of named entities Index (rdfs:label) Entity Search Top-k labels for each surface form Resource description f Surface form, entity type & context KB Entity Disambiguation Resource foreach label DBpedia Titles files and NLP resources available at: http://wiki.dbpedia.org/Downloads2015-04 Entity Linking Named Entity Recognition Tweet Improvement of NER Resource for surface form
- 17. 17 Classifiable Named Entity Linking Level ExampleLinking (%) Entity DBpedia Type Linkable Correctly Linked Wisconsin Geo- Location 63.11 Incorrectly Linked America Movie 3.05 Uninformative N.J. Thing 16.15 Non- Linkable Uninformative Secrets Thing 11.85 Generic Whitney Other 5.83 A total of 1442 entities out of 1496 entities are disambiguated with ~4k candidate KB resources Entity Linking-Performance Analysis Matching function, PKB (e, rc), to detect the resource for a surface form of named entity in KB, if it exists: 1. Lexical Similarity, lex(e, lrc) 2. Coherence, coh(e+, drc) Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 Experimental Analysis: Entity Linking ⇒ PKB (e, rc) = *(lex(e, lrc)) + (1- )*(coh(e+, drc)) ( currently set to 0.5)
- 18. 18 ENTITY RECOGNITION ENHANCEMENT Manchandaet al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 Surface forms of named entities Index (rdfs:label) Entity Search Top-k labels for each surface form Resource description f Surface form, entity type & context KB Entity Disambiguation Resource foreach label Entity Linking Named Entity Recognition Tweet T-NER+ Resource for surface form c* e = argmax {PCRF (e, c)*PKB (e, rc)} c
- 19. T-NER Performance Analysis T-NER+ Performance Analysis EntityType Precision Recall F1 Precision Recall F1 Band 0.26 0.88 0.40 0.39 0.90 0.54 Company 0.78 0.90 0.84 0.81 0.90 0.85 Facility 0.45 0.72 0.55 0.50 0.72 0.59 Geo-Location 0.80 0.95 0.87 0.80 0.95 0.87 Movie 0.24 0.88 0.38 0.34 0.88 0.49 Other 0.57 0.70 0.63 0.56 0.76 0.64 Person 0.72 0.92 0.81 0.77 0.92 0.84 Product 0.60 0.69 0.65 0.63 0.71 0.67 Sportsteam 0.52 0.83 0.64 0.63 0.85 0.72 TVshow 0.51 0.91 0.66 0.45 0.89 0.59 Overall 0.62 0.87 0.73 0.66 0.88 0.76 Comparative Analysis: T-NER and T-NER+ 19 Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015 Experimental Analysis: Entity Recognition Enhancement Entity Ground-Truth T-NER T-NER+ 30stm Band Product Band Yahoo Company Band Company Southgate House Facility Band Facility Canada Geo-Location Person Geo-Location Camp rock 2 Movie Person Movie Thanksgiving Other Person Other John Acuff Person Facility Person iphone Product Company Product Lions Sportsteam Person Sportsteam TMZ TVshow Band TVshow Example: Re-classification of entities Precision (P) = |{cor.cl} ∩ {cl}| |{cl}| Recall (R) = |{cor.cl} ∩ {cl}| |{cor. cl}| F1 Measure = 2 x P x R P+R cor.cl denotes correctly classified entities, while cl denotes classified entities.
- 20. 20 New knowledgeemerges constantly on social media streams Its important to identify new knowledge in order to bridge the gap between Unstructured Web and Web of Data An end-to-end entity linking pipeline might be helpful for detecting new knowledge Entity linking can be used to improve classification performance of an entity recognition system Improving entity recognition is crucial for identifying new entities Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015
- 21. 21 Presented anend-to-end entity linking pipeline for short textual formats (microposts) Presented an approach for improving entity recognition through re-classification Marginal improvements observed in re-classification using linked entities A definite scope for improving the current system New knowledge has been identified, though not dealt with currently Quality assessment, trustworthy factors… Relation extraction from microposts to improve identification of new knowledge Experimenting with more recent datasets Manchanda et al., Leveraging Entity Linking for Entity Recognition in Microposts KDIR 2015