Simple Large-scale Relation Extraction from Unstructured Text
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1 Simple Large-scale Relation Extraction from Unstructured Text Christos Christodoulopoulos and Arpit Mittal Amazon Research Cambridge
2 Alexa Question Answering Alexa, what books did Carrie Fisher write? The books that Carrie Fisher is an author of are Delusions of Grandma, Shockaholic, Surrender the Pink, Postcards from the Edge, The Best Awful There Is and Wishful Drinking. 2
3 Alexa Knowledge Base Named relations between entities Carrie Fisher is the author of Postcards from the Edge is an instance of book 3
4 Alexa Knowledge Base 4
5 Alexa Knowledge Base Sources of knowledge: 1. Human authorship 2. Structured information 3. Unstructured information 5
6 Knowledge from Unstructured Text The Goal: Carrie Fisher wrote several semi-autobiographical novels, including Postcards from the Edge. 6
7 Knowledge from Unstructured Text The Goal: Carrie Fisher wrote several semi-autobiographical novels, including Postcards from the Edge. 7
8 Knowledge from Unstructured Text The Goal: Carrie Fisher wrote several semi-autobiographical novels, including Postcards from the Edge. Entity Recognition Entity Resolution Relation Extraction Likelihood Estimation 8
9 Knowledge from Unstructured Text The Goal: Carrie Fisher wrote several semi-autobiographical novels, including Postcards from the Edge. [carrie fisher] [is th e of] [postcards from the edge] Entity Recognition Entity Resolution Relation Extraction Likelihood Estimation 9
10 Knowledge from Unstructured Text The Goal: Carrie Fisher wrote several semi-autobiographical novels, including Postcards from the Edge. [carrie fisher] [is the author of] [postcards from the edge] Entity Recognition Entity Resolution Relation Extraction Likelihood Estimation 10
11 Knowledge from Unstructured Text The Goal: Carrie Fisher wrote several semi-autobiographical novels, including Postcards from the Edge. [carrie fisher] [is the author of] [postcards from the edge] Ontological constraints Entity embeddings Distributional information 98% likelihood Entity Recognition Entity Resolution Relation Extraction Likelihood Estimation 11
12 Relation Extraction Approaches Rule-based Fully supervised Unsupervised Distant/weakly supervised Snow, Jurafsky, Ng, 2005 Main assumption: if two entities are linked by a relation, any sentence containing both sentences is likely to express that relation [steven spielberg] [is the director of] [saving private ryan] Spielberg s film Saving Private Ryan is based on 12
13 Distant supervision label generation Wikipedia Chunking PoS Tagging Entity denotations (surface forms) Gazetteers Entity pairs (KB IDs) 13
14 Distant supervision label generation Wikipedia Chunking PoS Tagging Entity denotations (surface forms) Gazetteers Entity pairs (KB IDs) Ontological Constraints Check against KB YES Positive Label NO Negative Label 14
15 Distant supervision label generation His studies were interrupted by army service and at the end of the war he was forced to return... [the second world war] [is an instance of] [cause of death] In the intro to the song, Fred Durst makes reference to... [intro 15367][is an instance of] [song] Turner also released one album and several singles under the moniker Repeat. [the singles the 2011 album] [is an instance of] [album] Wikipedia Chunking PoS Tagging Entity denotations (surface forms) Gazetteers Entity pairs (KB IDs) Ontological Constraints Check against KB YES Positive Label NO Negative Label 15
16 Distant supervision label generation Wikipedia page URL à KB ID lookup Main entity (KB ID) KB Related entities (x) (KB IDs) rel(x 1, main) rel(main, x 2 ) KB ID à Denotations lookup Entity denotations (x + main strings) Wikipedia Chunking PoS Tagging Entity denotations (surface forms) Gazetteers Entity pairs (KB IDs) Ontological Constraints Check against KB YES Positive Label NO Negative Label 16
17 Distant supervision label generation Wikipedia page Wikipedia URL à KB ID lookup Phylo (Video Game) Catch Me If You Can McGill University Montréal Masters Degree Chunking PoS Tagging human Main entity (KB ID) Concordia University McGill University Montreal Quebec Concordia University Human Being TV Appearance Role KB Judge judge George Springate Related entities (x) (KB IDs) rel(x 1, main) rel(main, x 2 ) Politician Entity denotations (surface forms) Columnist politician Footballer Journalist columnist Occupation journalist KB ID à Denotations lookup football player Gazetteers Athlete The Chicago Tribune Entity pairs (KB IDs) Entity denotations (x + main strings) person Lawyer lawyer African- American Ontological Constraints Law Firm Office Check against KB (Bloom filters) Randy Cohen YES NO Positive Label Negative Label 17
18 Distant supervision label generation Wikipedia page URL à KB ID lookup Main entity (KB ID) KB Related entities (x) (KB IDs) rel(x 1, main) rel(main, x 2 ) KB ID à Denotations lookup Entity denotations (x + main strings) Call Your Girlfriend was written by Robyn, Alexander Kronlund and Klas Åhlund, with the latter producing Chunkingthe song. Entity denotations Entity pairs Wikipedia [call your girlfriend PoS 3] [is Gazetteers (surface an instance forms) of] [song] (KB IDs) Forget Her is a song Tagging by Jeff Buckley. [forget her] [is an instance of] [song] The Subei Mongol Autonomous County is an autonomous county within the prefecture-level city of Jiuquan in the northwestern Chinese province of Gansu. Positive [subei mongol autonomous county] [is an instance YES of] [chinese county] Ontological Constraints Check against KB (Bloom filters) NO Label Negative Label 18
19 Relation extraction HypeNET (Shwartz and Goldberg, 2016) Hyponyms [is an instance of] only LexNET extends to multiple relations Embeddings lemma support 1 LSTM average pooling left entity distr. vector POS dependency label direction X/NOUN/nsubj/> be/verb/root/- Y/NOUN/attr/< support 2 LSTM X/NOUN/dobj/> define/verb/root/- as/adp/prep/< Y/NOUN/pobj/< right entity distr. vector 19
20 Results Alexa KB Relation HypeNET [is an instance of] (0.21) [is the birthplace of] (0.26) [applies to] (1.78) 20
21 Results Alexa KB Relation HypeNET [is an instance of] (0.21) [is the birthplace of] (0.26) [applies to] (1.78) Wikidata Relation HypeNET instance of (P31) (0.21) birthplace of (P19) (0.90) part of (P527) (2.59) 21
22 Relation extraction token 4-grams fasttext (Joulin et al., 2016) Linear model One hidden layer Rank constraint support 1 tokens left lemma 0110 X/0000/NOUN/nsubj/> be/0010/verb/root/- Y/0000/NOUN/attr/< right lemma 0100 hidden layer (binary) classification X/0000/NOUN/dobj/> define/1010/verb/root/- as/adp/prep/< Y/0000/NOUN/pobj/< right lemma 0100 support 2 tokens 22
23 Alexa KB Results HypeNET equally good as the much simpler fasttext with the same input features. Relation HypeNET fasttext [is an instance of] (0.21) (0.03) [is the birthplace of] (0.26) (0.01) [applies to] (1.78) (0.01) 23
24 Alexa KB Results HypeNET equally good as the much simpler fasttext with the same input features. Relation HypeNET fasttext [is an instance of] (0.21) (0.03) [is the birthplace of] (0.26) (0.01) [applies to] (1.78) (0.01) Wikidata Relation HypeNET fasttext instance of (P31) (0.21) (0.01) birthplace of (P19) (0.90) (0.07) part of (P527) (2.59) (0.16) 24
25 Alexa KB Results HypeNET equally good as the much simpler fasttext with the same input features. MaxEnt results show that features alone are not enough. Need to create higher-dimensional representations of discrete features. Relation HypeNET fasttext MaxEnt [is an instance of] (0.21) (0.03) [is the birthplace of] (0.26) (0.01) [applies to] (1.78) (0.01) Wikidata Relation HypeNET fasttext MaxEnt instance of (P31) (0.21) (0.01) birthplace of (P19) (0.90) (0.07) part of (P527) (2.59) (0.16)
26 Summary New method for entity resolution Page-specific gazetteers Features are important HypeNET vs fasttext Feature representation is important fasttext vs MaxEnt 26
27 Future directions Enhanced entity recognition Use of human annotation for seeding supervision Expanding to multiple sources of text Coverage of multiple languages 27
28 Thanks! 28
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