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Joint Entity and Relation Extraction using Card-Pyramid Parsing . Rohit J. Kate Raymond J. Mooney. Entity and Relation Extraction. Information Extraction is the task of extracting structured information from text Entity Extraction Person, Location, Organization Relation Extraction

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Joint Entity and Relation Extraction using Card-Pyramid Parsing


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joint entity and relation extraction using card pyramid parsing

Joint Entity and Relation Extraction using Card-Pyramid Parsing

Rohit J. Kate

Raymond J. Mooney

entity and relation extraction
Entity and Relation Extraction
  • Information Extraction is the task of extracting structured information from text
  • Entity Extraction

Person, Location, Organization

  • Relation Extraction

Located_In(Location, Location)

Work_For(Person, Organization)

OrgBased_In(Organization, Location)

Live_In(Person, Location)

Kill(Person, Person)

entity and relation extraction3

Work_For

OrgBased_In

Live_In

OrgBased_In

Located_In

Live_In

Person

Location

Location

Other

Organization

Entity and Relation Extraction

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

entity and relation extraction4
Entity and Relation Extraction
  • Traditionally, entity and relation extraction is done in a pipeline
  • First entities are extracted
  • Then relations are extracted assuming that the extracted entities are correct
entity and relation extraction5
Entity and Relation Extraction
  • However, relations can influence entity extraction

Live_In

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Person?

Location?

Person

Location

entity and relation extraction6

OrgBased_In

Entity and Relation Extraction
  • Relations can also influence extracting other relations

Work_For

Live_In

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Person

Location

Organization

joint entity and relation extraction
Joint Entity and Relation Extraction
  • Both entity and relation extraction can benefit if done jointly
    • Correct errors of each other
    • Influence each other
  • A brute force algorithm to find the most probable joint extraction is intractable
    • If there are n entities in a sentence then O(n2) possible relations between them and for r relation labels O(rn^2) possibilities
  • We present a new method for joint extraction
joint entity and relation extraction8
Joint Entity and Relation Extraction
  • Treat it analogous to parsing with the following productions:
    • Entity productions:
      • Person  Candidate_entity
      • Location  Candidate_entity
      • Organization  Candidate_entity
    • Relation productions:
      • Located_In Location Location
      • Work_For Person Organization
      • OrgBased_In Organization Location
      • Live_InPerson Location
      • KillPerson Person
joint entity and relation extraction9

Work_For

OrgBased_In

Live_In

OrgBased_In

Located_In

Live_In

Person

Location

Location

Other

Organization

Joint Entity and Relation Extraction
  • However, many entities are in multiple relations, with a lot of overlapping
  • Context-free grammar (CFG) tree structure is not adequate
  • We introduce a new structure we call card-pyramid

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

joint entity and relation extraction using card pyramid

Person

Location

Location

Other

Organization

Person

Location

Organization

Location

Other

Joint Entity and Relation Extraction using Card-Pyramid

Work_For

OrgBased_In

Live_In

OrgBased_In

Located_In

Live_In

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Work_For

OrgBased_In

Not_Related

Live_In

Not_Related

OrgBased_In

Located_In

Live_In

Not_Related

Not_Related

Candidate entities

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

joint entity and relation extraction using card pyramid11
Joint Entity and Relation Extraction using Card-Pyramid
  • Entities and their relations are compactly represented in a card-pyramid graph
  • Joint entity and relation extraction reduces to finding the most probable joint labeling of its nodes
  • We developed an efficient bottom-up card-pyramid parsing algorithm which uses dynamic programming and beam search, given entity and relation classifiers
distinction from cfg tree
Distinction from CFG Tree

CFG Tree

Card-Pyramid

No overlap

Overlap

distinction from cfg tree13
Distinction from CFG Tree

CFG Tree

Card-Pyramid

No overlap

Overlap

card pyramid parsing

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing
  • Assumes candidate entities are given
    • Can be obtained automatically [Punyakanok & Roth, 2001]
    • Use a simple heuristic, like all noun-phrase chunks
    • In the worst case include every substring, they will get label Other if they are none of the given types

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

card pyramid parsing15

Beam

Beam element

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Apply entity classifiers at the leaf nodes

SVM with standard features: words, POS tags,

capitalization, gazetteer, suffixes etc.

card pyramid parsing16

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Apply relation classifiers bottom-up at

the internal nodes relating leftmost and

rightmost leaves

SVM with word subsequence kernel for before,

between and after patterns of the two entities

[Bunescu & Mooney, 2005]

card pyramid parsing17

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Consider every combination

of children’s beam elements

card pyramid parsing18

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Work_For Per Org

card pyramid parsing19

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Work_For Per Org

card pyramid parsing20

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

card pyramid parsing21

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Located_In Loc Loc

card pyramid parsing22

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

card pyramid parsing23

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Live_In Per Loc

card pyramid parsing24

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

…………..

card pyramid parsing25

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

…………..

card pyramid parsing26

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

…………..

card pyramid parsing27

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Relations with in-between entities are

also used as features, for e.g.

“Live_In -- Located_In”

In general, any features can be

used from the sub-card-pyramid

underneath.

Live_In Per Loc

…………..

card pyramid parsing28

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Divide the probability which gets

multiplied twice.

Live_In Per Loc

…………..

card pyramid parsing29

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

A beam element represents a

sub-card-pyramid.

…………..

card pyramid parsing30

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

…………..

card pyramid parsing31

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

An O(1) check for

consistency of overlap.

…………..

X

card pyramid parsing32

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

An O(1) check for

consistency of overlap.

X

…………..

card pyramid parsing33

(Austin)

(Los Angeles)

(California)

(American)

(ABC Inc)

Card-Pyramid Parsing

Austin lives in Los Angeles, California and works there for an American company called ABC Inc.

Most probable card-pyramid

is represented by the top

beam element at the root.

An approximation because

of the finite beam size.

…………..

training classifiers
Training Classifiers
  • Obtain correctly labeled card-pyramids for the annotated sentences in the training data
  • Collect the positive examples for all the classifiers from the labels of the card-pyramids
  • Positive examples for an entity classifier become negative examples for all other entity classifiers
  • Pairs of entities with correct entity types but not related by a relation become negative examples for that relation’s classifier
related work
Related Work
  • Roth & Yih [2004, 2007]
    • Employs independent entity and relation classifiers
    • Uses linear programming to find a consistent global solution from the classifier outputs
    • Output of other classifiers can’t be used as features
  • Riedel et al. [2009]
    • Solves a related problem of extracting bio-molecular events and their arguments using Markov Logic Network
    • Single joint probabilistic model
    • Restricts extractors’ learning algorithm to Markov Logic Network’s learning algorithm, for example, cannot use kernel-based SVM for relation extraction
  • Kate & Mooney [2006]
    • Parse using a suite of classifiers to find the most probable semantic parse
experiments
Experiments
  • Dataset used by Roth & Yih [2004, 2007]
  • Number of sentences: 1437
  • Entities:

Person (1685), Location (1968), Organization (978), Other (705)

  • Relation Extraction

Located_In(Location, Location) (406)

Work_For(Person, Organization) (394)

OrgBased_In(Organization, Location) (451)

Live_In(Person, Location) (521)

Kill(Person, Person) (268)

Not_Related (17007)

experiments37
Experiments
  • Performed five-fold cross-validation
  • Measured:
    • Precision (percentage of output labels correct)
    • Recall (percentage of gold-standard labels correctly identified)
    • F-measure (harmonic mean)
  • Compared with:
    • A pipelined approach using our entity and relation classifiers
    • Best results of Roth & Yih [2007] on joint extraction
results relation extraction

An unusual sentence

with 20 Locations

separated by commas.

Results: Relation Extraction

F-measure

a general method to extract structured information from a sentence
A General Method to Extract Structured Information from a Sentence
  • Encode what you want to extract and constraints between them in the productions
  • Train a classifier for every production
  • Apply the classifiers to find the most probable structure allowed by the productions to jointly find the structured information
future work
Future Work
  • Extract higher order relations: relations between relations such as temporal or causal relations
  • Jointly perform co-reference resolution with entity and relation extraction
    • Add a new production: CorefPerson Person
  • Model the structure of card-pyramid using a probabilistic graphical model
  • A kernel to compute similarity between two card-pyramids and use it for relation classifier
conclusions
Conclusions
  • Introduced a card-pyramid structure for joint entity and relation extraction
  • Compactly encode entities and relations in a sentence
  • Joint extraction reduces to jointly labeling the nodes
  • Presented an efficient parsing algorithm for joint labeling
  • Experiments demonstrated benefits of the approach
thanks
Thanks!

Questions?