(open) information extraction: overview and recent advancesdellibovi/talks/seminar_oie.pdf · the...
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Final Projects
(Open) Information Extraction:Overview and Recent AdvancesClaudio Delli Bovi
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Where do we go now?
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 2
You’ve seen many powerful methods for NLP:
- Language models, Part-of-Speech tagging, Syntactic Parsing, …
- Word Sense Disambiguation, Machine Translation, Semantic Similarity, ...
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Where do we go now?
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 2
You’ve seen many powerful methods for NLP:
- Language models, Part-of-Speech tagging, Syntactic Parsing, …
- Word Sense Disambiguation, Machine Translation, Semantic Similarity, ...
The goal: Machine Reading
- Autonomous understanding of unstructured text on a large scale
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Where do we go now?
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 2
You’ve seen many powerful methods for NLP:
- Language models, Part-of-Speech tagging, Syntactic Parsing, …
- Word Sense Disambiguation, Machine Translation, Semantic Similarity, ...
The goal: Machine Reading
- Autonomous understanding of unstructured text on a large scale
“I hereby offer to bet anyone a lobster dinner that by 2015 we will have a computer program capable of automatically reading at least 80% of the factual content across the entire English-speaking web, and placing those facts in a structured knowledge base.”(T. Mitchell. Reading the Web: A Breakthrough Goal for AI. AI Magazine, 2005)
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Where do we go now?
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 3
Machine Reading is an ill-posed problem:
Human Reading
✓ high precision✓ broad scope✓ high comprehension✓ background knowledge❌ sentence-by-sentence❌ (usually) single language❌ slow
Machine Reading
❌ noisy❌ limited scope❌ minimal reasoning❌ bottom up✓ corpus-wide statistics✓ (possibly) multilingual✓ very fast!
O. Etzioni, M. Banko, M.J. Cafarella. Machine Reading. AAAI, 2007.
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 4
“A process of getting structured data from unstructured information in the text”(Jurafsky and Martin, 2009)
“Identification of instances of a particular class of relationships in a natural language text, and the extraction of relevant arguments for that relationships”(Grishman, 1997)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 5
Traditional Information Extraction pipeline:
(Jurafsky and Martin, 2009)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 5
Traditional Information Extraction pipeline:
(Jurafsky and Martin, 2009)
1. Named Entity Recognition (homework 3, anyone?)
(Jurafsky and Martin, 2009)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 5
Traditional Information Extraction pipeline:
1. Named Entity Recognition (homework 3, anyone?)
2. Relation Extraction
spokesman of
part of
part of
fare increase
( American Airlines , part of , AMR Corp. )
( Tim Wagner , spokesman of , AMR Corp. )
( United , part of , UAL Corp. )
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 6
Yes, but… which (and how many) relations?
- Restricted set of semantic relations handcrafted by humans(either general or domain-specific)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 6
Yes, but… which (and how many) relations?
- Wikipedia infoboxes!
- Restricted set of semantic relations handcrafted by humans(either general or domain-specific)
( Sapienza , Rector , Eugenio Gaudio )
( Sapienza , Location , Rome )
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 6
Yes, but… which (and how many) relations?
- Wikipedia infoboxes!
- Restricted set of semantic relations handcrafted by humans(either general or domain-specific)
Crowdsourced ontology derived (mainly) from Wikipedia infoboxes and containing over 2 billion RDF triples.http://wiki.dbpedia.org
http://www.freebase.com
people/person/nationality
location/location/contains
people/person/place-of-birth
...
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 6
Yes, but… which (and how many) relations?
- Wikipedia infoboxes
- Restricted set of semantic relations handcrafted by humans(either general or domain-specific)
- Ontological relations from thesauri like WordNet
is a, instance of (hypernymy)
part of (meronymy)
...
entailment
pertanymy
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 7
Hand-written patterns and rules
Supervised learning algorithms
Semi-supervised learning algorithms
Plenty of approaches:
Weak and distant supervision
Unsupervised algorithms(Open Information Extraction)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 8
Hand-written patterns and rules (Hearst, 1992)
(Jurafsky and Martin, 2009)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 9
Supervised Learning (Zhao and Grishman, 2005; Bunescu and Mooney, 2006)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 9
- Start from a fixed set of relations and entities
Supervised Learning (Zhao and Grishman, 2005; Bunescu and Mooney, 2006)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 9
- Start from a fixed set of relations and entities
- Use these to annotate a large enough training corpusPositive examples: annotated triples ⟨entity, relation, entity⟩ Negative examples: generated from non-annotated within-sentence entity pairs
Supervised Learning (Zhao and Grishman, 2005; Bunescu and Mooney, 2006)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 9
Supervised Learning (Zhao and Grishman, 2005; Bunescu and Mooney, 2006)
- Start from a fixed set of relations and entities
- Use these to annotate a large enough training corpus
- Train a classifier to annotate unseen text
Positive examples: annotated triples ⟨entity, relation, entity⟩ Negative examples: generated from non-annotated within-sentence entity pairs
Word features (bag-of-words, headwords, bigrams…)
NE features (entity types and their concatenation)
Syntactic features (constituents, dependency paths, ...)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 10
Semi-supervised/Weakly Supervised Learning (Kozareva and Hovy, 2010)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 10
Start from very few high-precision seed patterns (or seed triples)
“ [...] Ryanair has a hub at Charleroi.”
(Ryanair, hub, Charleroi)
Semi-supervised/Weakly Supervised Learning (Kozareva and Hovy, 2010)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 10
“ [...] Ryanair has a hub at Charleroi.”
(Ryanair, hub, Charleroi)
Bootstrapping:
“Budget airline Ryanair, which uses Charleroi as a hub, scrapped all weekend flights out of the airport”
“All flights in and out of Ryanair’s Belgian hub at Charleroi airport were grounded on Friday”
“A spokesman at Charleroi, a main hub for Ryanair, estimated that 8000 passengers had already been affected”
1. Find sentences that contain both entities
Semi-supervised/Weakly Supervised Learning (Kozareva and Hovy, 2010)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 10
Semi-supervised/Weakly Supervised Learning (Kozareva and Hovy, 2010)
“ [...] Ryanair has a hub at Charleroi.”
(Ryanair, hub, Charleroi)
Bootstrapping:
“Budget airline Ryanair, which uses Charleroi as a hub, scrapped all weekend flights out of the airport”
“All flights in and out of Ryanair’s Belgian hub at Charleroi airport were grounded on Friday”
“A spokesman at Charleroi, a main hub for Ryanair, estimated that 8000 passengers had already been affected”
1. Find sentences that contain both entities
2. Generalize to new patterns
/[ORG], which uses [LOC] as a hub/
/[ORG]'s hub at [LOC]/
/[LOC] a main hub for [ORG]/
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 11
Self-supervised Learning (Etzioni et al., 2005; Weld et al., 2008)
Idea: use the new patterns to search for additional triples and build a self-labeled training dataset
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 11
Self-supervised Learning (Etzioni et al., 2005; Weld et al., 2008)
Issues: error propagation, semantic drift (erroneous patterns leads to the introduction of erroneous tuples, which, in turn...)
Idea: use the new patterns to search for additional triples and build a self-labeled training dataset
/[ORG] has a hub at [LOC]/ “ [...] Sydney has a ferry hub at Circular Quay”
(Sydney, hub, Circular Quay)?
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 12
Web-scale self-supervised learning system, running at CMUcontinuously 24 hours per day
NELL - Never Ending Language Learning (Carlson et al., 2010)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 12
Requires:- An initial ontology with categories (person,sportsTeam,
fruit, ...) and relations (playsInstrument, playsOnTeam, ...)
NELL - Never Ending Language Learning (Carlson et al., 2010)
Web-scale self-supervised learning system, running at CMUcontinuously 24 hours per day
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 12
- 10-15 seed examples of each category and relation
NELL - Never Ending Language Learning (Carlson et al., 2010)
Requires:- An initial ontology with categories (person,sportsTeam,
fruit, ...) and relations (playsInstrument, playsOnTeam, ...)
Web-scale self-supervised learning system, running at CMUcontinuously 24 hours per day
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 13
Uses a variety of methods to extract beliefs from the web
Hundreds of different extraction modules simultaneously trained:
- Co-occurrence based pattern learners
- HTML lists and tables miners
- “Traditional” classifiers on various features
NELL - Never Ending Language Learning (Carlson et al., 2010)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 13
..and a bit of human supervision once in a while!
Hundreds of different extraction modules simultaneously trained...
NELL - Never Ending Language Learning (Carlson et al., 2010)
Uses a variety of methods to extract beliefs from the web
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 14
NELL - Never Ending Language Learning (Carlson et al., 2010)
http://rtw.ml.cmu.edu/rtw/
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 15
Distantly Supervised Learning (Mintz et al., 2009; Riedel et al., 2010, Hoffmann et al., 2011)
Instead of just a handful of seeds, use a large knowledge base (possibly human-contributed) to acquire many reliable training examples
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 15
Distantly Supervised Learning (Mintz et al., 2009; Riedel et al., 2010, Hoffmann et al., 2011)
Instead of just a handful of seeds, use a large knowledge base (possibly human-contributed) to acquire many reliable training examples
For each entity pair, identify all sentences mentioning them in a massive unlabeled corpus
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 15
Distantly Supervised Learning (Mintz et al., 2009; Riedel et al., 2010, Hoffmann et al., 2011)
Instead of just a handful of seeds, use a large knowledge base (possibly human-contributed) to acquire many reliable training examples
Lots of noisy pattern features, then combined in a supervised classifier
For each entity pair, identify all sentences mentioning them in a massive unlabeled corpus
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 16
Distantly Supervised Learning (Mintz et al., 2009; Riedel et al., 2010, Hoffmann et al., 2011)
Distant supervision assumption:If two entities participate in a relation, all sentences that mention these two entities express that relation
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 16
Distantly Supervised Learning (Mintz et al., 2009; Riedel et al., 2010, Hoffmann et al., 2011)
Distant supervision assumption:If two entities participate in a relation, all sentences that mention these two entities express that relation
(Brad Pitt, married with, Angelina Jolie)
“ Brad Pitt will be starring with Angelina Jolie in ‘World War X’ ”
“Angelina Jolie joins Brad Pitt for first public appearance [...]”
“Angelina Jolie expects another baby with Brad Pitt”
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 17
Distantly Supervised Learning (Mintz et al., 2009; Riedel et al., 2010, Hoffmann et al., 2011)
Relax the assumption:- If two entities participate in a relation, all sentences at least
one sentence that mention these two entities express that relation
Multi-instance
(expressed-at-least-once assumption)
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Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 17
Distantly Supervised Learning (Mintz et al., 2009; Riedel et al., 2010, Hoffmann et al., 2011)
Relax the assumption:- If two entities participate in a relation, all sentences at least
one sentence that mention these two entities express that relation
Multi-instance
- Allow for some relations to overlap:(Steve Jobs, founded, Apple) (Steve Jobs, CEO of, Apple)
Multi-label Learning (Surdeanu et al., 2012)
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Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 18
A way more radical approach:
no predefined set of relations
no human intervention
no training data
only a large unlabeled corpus (like the Web) as input
Unsupervised Learning (Banko and Etzioni, 2008; Wu and Weld, 2010)
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Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 18
“Open Information Extraction (OIE) [...] a novel extraction paradigm that facilitates domain-independent discovery of relations extracted from text and readily scales to the diversity and size of the Web corpus. The sole input to an OIE system is a corpus, and its output is a set of extracted relations. An OIE system makes a single pass over its corpus guaranteeing scalability with the size of the corpus.”(Banko et al., 2007)
OIE system
Web-scale unlabeled corpus
Unstructured set of relation instances (triples)
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Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 19
ReVerb (Fader et al., 2011)
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Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 19
ReVerb (Fader et al., 2011)
Given a sentence s:- POS tagging and chunking over s;
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Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 19
ReVerb (Fader et al., 2011)
Given a sentence s:- POS tagging and chunking over s;
- For each verb in s, find the longest sequence of words w that starts with that verb and satisfies syntactic and lexical constraints;
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Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 19
ReVerb (Fader et al., 2011)
Given a sentence s:- POS tagging and chunking over s;
- For each verb in s, find the longest sequence of words w that starts with that verb and satisfies syntactic and lexical constraints;
- For each phrase w, find the nearest NP to the left and to the right;
![Page 45: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/45.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 19
ReVerb (Fader et al., 2011)
Given a sentence s:- POS tagging and chunking over s;
- For each verb in s, find the longest sequence of words w that starts with that verb and satisfies syntactic and lexical constraints;
- For each phrase w, find the nearest NP to the left and to the right;
- Assign a confidence c to the relation r = (x, w, y) using a confidence classifier.
![Page 46: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/46.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 20
ReVerb (Fader et al., 2011)
s: United has a hub in Chicago, which is the headquarters of United Continental Holdings.
r1: (United, has a hub in, Chicago)
r2: (Chicago, is the headquarters of, United Continental Holdings)
![Page 47: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/47.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 20
ReVerb (Fader et al., 2011)
s: United has a hub in Chicago, which is the headquarters of United Continental Holdings.
r1: (United, has a hub in, Chicago)
r2: (Chicago, is the headquarters of, United Continental Holdings)
Almost 15 million extractions (1.3 million distinct relations) from the ClueWeb09 dataset!
![Page 48: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/48.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 21
OIE is great, but...
![Page 49: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/49.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 21
OIE is great, but...
Sparsity: many relation phrases actually express the same relationship (e.g. synonyms, paraphrases)
![Page 50: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/50.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 21
OIE is great, but...
Arguments (and relation phrases) are ambiguous
Sparsity: many relation phrases actually express the same relationship (e.g. synonyms, paraphrases)
![Page 51: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/51.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 21
OIE is great, but...
Arguments (and relation phrases) are ambiguous
Sparsity: many relation phrases actually express the same relationship (e.g. synonyms, paraphrases)
We need semantics!
![Page 52: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/52.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 22
PATTY (Nakashole et al., 2012)
![Page 53: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/53.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 22
PATTY (Nakashole et al., 2012)
From patterns to pattern synsets (clusters of relation phrases that express the same relation):
{ settled in , live in , moved to , stayed in , in area of , ...}
![Page 54: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/54.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 22
PATTY (Nakashole et al., 2012)
From patterns to pattern synsets (clusters of relation phrases that express the same relation)
Each pattern synset (= relation) has semantic types:
film/actor already played with film/actor
music/artist already played with music/composer
...
![Page 55: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/55.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 22
PATTY (Nakashole et al., 2012)
From patterns to pattern synsets (clusters of relation phrases that express the same relation)
Each pattern synset (= relation) has semantic types
Patterns are hierarchically organized in a taxonomy:
{ is romantically involved with , is dating }
{ knows , ... }⊂
![Page 56: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/56.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 23
PATTY (Nakashole et al., 2012)
Generalized syntactic-ontological-lexical (SOL) patterns:
Amy Winehouse’s soft voice in ‘Rehab’
Elvis Presley’s solid voice in his song ‘All shook up’
![Page 57: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/57.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 23
PATTY (Nakashole et al., 2012)
Generalized syntactic-ontological-lexical (SOL) patterns:
Amy Winehouse’s soft voice in ‘Rehab’
Elvis Presley’s solid voice in his song ‘All shook up’
Detect and link entities
![Page 58: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/58.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 23
PATTY (Nakashole et al., 2012)
Generalized syntactic-ontological-lexical (SOL) patterns:
<Person> ’s [ ADJ ] voice * <Song>
Amy Winehouse’s soft voice in ‘Rehab’
Elvis Presley’s solid voice in his song ‘All shook up’
Generalize SOL patterns
![Page 59: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/59.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 23
PATTY (Nakashole et al., 2012)
Generalized syntactic-ontological-lexical (SOL) patterns:
<Person> ’s [ ADJ ] voice * <Song>
(Elvis Presley, All Shook Up)
(Amy Winehouse, Rehab)Amy Winehouse’s soft voice in ‘Rehab’
Elvis Presley’s solid voice in his song ‘All shook up’ Extract relation instances
(support set)
![Page 60: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/60.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 24
PATTY (Nakashole et al., 2012)
Generalized syntactic-ontological-lexical (SOL) patterns:
<Person> ’s [ ADJ ] voice * <Song>
Lexical word features (L) harvested from an input corpora
![Page 61: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/61.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 24
PATTY (Nakashole et al., 2012)
Generalized syntactic-ontological-lexical (SOL) patterns:
<Person> ’s [ ADJ ] voice * <Song>
Syntactic modifiers (S) are generalized using POS tags or wildcards
![Page 62: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/62.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 24
PATTY (Nakashole et al., 2012)
Generalized syntactic-ontological-lexical (SOL) patterns:
<Person> ’s [ ADJ ] voice * <Song>
Ontological semantic types (O) from a knowledge base
![Page 63: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/63.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 25
WiSeNet (Moro and Navigli, 2012; 2013)
![Page 64: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/64.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 25
WiSeNet (Moro and Navigli, 2012; 2013)
Wikipedia-based Semantic Network:
![Page 65: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/65.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 26
WiSeNet (Moro and Navigli, 2012; 2013)
Filtering out bad relational phrases:
Wiesbaden State Library is funded by the State of Hesse and located in Wiesbaden.
![Page 66: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/66.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 26
WiSeNet (Moro and Navigli, 2012; 2013)
Filtering out bad relational phrases:
( Wiesbaden State Library , is funded by the , State of Hesse )
Wiesbaden State Library is funded by the State of Hesse and located in Wiesbaden.
![Page 67: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/67.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 26
WiSeNet (Moro and Navigli, 2012; 2013)
Filtering out bad relational phrases:
( Wiesbaden State Library , is funded by the , State of Hesse )
Wiesbaden State Library is funded by the State of Hesse and located in Wiesbaden.
( Wiesbaden State Library , is funded by the State of Hesse and located in , Wiesbaden )
![Page 68: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/68.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 26
WiSeNet (Moro and Navigli, 2012; 2013)
Filtering out bad relational phrases:
( Wiesbaden State Library , is funded by the , State of Hesse )
Wiesbaden State Library is funded by the State of Hesse and located in Wiesbaden.
( Wiesbaden State Library , is funded by the State of Hesse and located in , Wiesbaden )
( State of Hesse , and located in , Wiesbaden )
![Page 69: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/69.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 27
WiSeNet (Moro and Navigli, 2012; 2013)
How? Use syntactically-grounded patterns:
X is founded by Y X and located in Y
founded
X
Y
is by
located
X
Yand
innsubjpass
conjprepprep
pobj
pobj
cc
![Page 70: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/70.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 27
WiSeNet (Moro and Navigli, 2012; 2013)
How? Use syntactically-grounded patterns:
X is founded by Y X and located in Y
founded
X
Y
is by
located
X
Yand
innsubjpass
conjprepprep
pobj
pobj
cc
![Page 71: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/71.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 28
WiSeNet (Moro and Navigli, 2012; 2013)
Use soft clustering techniques to build relation synsets with ambiguous patterns:
{ is a part of , is a territory of , … , is a province of }
{ is a part of , is a member of , … , is an element of }
{ made her acting debut in the , made his professional debut in the , … }
{ used to build, used to construct , … , used to manufacture }
![Page 72: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/72.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 29
WiSeNet (Moro and Navigli, 2012; 2013)
Exploit Wikipedia categories to generate semantic types:
![Page 73: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/73.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 30
WiSeNet (Moro and Navigli, 2012; 2013)
Finally, use types to deal with ambiguous relation instances:
![Page 74: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/74.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 30
WiSeNet (Moro and Navigli, 2012; 2013)
Finally, use types to deal with ambiguous relation instances:
![Page 75: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/75.jpg)
Open Information Extraction
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 30
WiSeNet (Moro and Navigli, 2012; 2013)
Finally, use types to deal with ambiguous relation instances:
![Page 76: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/76.jpg)
What else?
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 31
![Page 77: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/77.jpg)
What else?
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 31
Put together syntactic and semantic analysis and generate semantically augmented patterns:
![Page 78: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/78.jpg)
What else?
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 31
![Page 79: (Open) Information Extraction: Overview and Recent Advancesdellibovi/talks/seminar_OIE.pdf · The goal: Machine Reading - Autonomous understanding of unstructured text on a large](https://reader035.vdocuments.net/reader035/viewer/2022063023/5ffc7e58851a3d6a65169e01/html5/thumbnails/79.jpg)
So… happy (knowledge) harvesting!
(Open) Information ExtractionClaudio Delli Bovi 29/05/2015 31