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An alignment-based Approach to Semi-supervised Relation Extraction
                   Including Multiple Arguments
                                                          Seokhwan Kim, Minwoo Jeong, Gary Geunbae Lee, Kwangil Ko, and Zino Lee
                                                               {megaup, stardust, gblee}@postech.ac.kr, {kik, zino}@alticast.com

  Abstract - We present an alignment-based approach to semi-supervised relation extraction task including more than two arguments. We concentrate
  on improving not only the precision of the extracted result, but also on the coverage of the method. Our relation extraction method is based on an
  alignment-based pattern matching approach which provides more flexibility of the method. In addition, we extract all relationships including two or
  more arguments at once in order to obtain the integrated result with high quality. We present experimental results which indicate the effectiveness of
  our method.

                                                                                                                            Alignment-based Information Extraction
v Information Extraction                                                                                        v Sentence Alignment for Information Extraction                                                                                 w Matrix Computation
w Extracting the defined number of relevant                                                                     w Example                                                                                                                                       M i 1, j 1 sim i
arguments from natural language documents                                                                                the character <ROLE> portrayed by <ACTOR> in the television series <PROGRAM> is
                                                                                                                                                                                                                                                                                                  1, j 1
                                                                                                                                                                                                                                                                M i 1, j gp
w Subtasks                                                                                                                                                                                                                                       M i, j     max
                                                                                                                                                                                                                                                                M i , j 1 gp
 # of arguments                                              subtask                                                                                                                                                                                            0
        1                                           named-entity recognition      character Michael Scofield portrayed by Wentworth Miller in the TV series Prison Break is


                                                                                                                                                                                                                                                              {
                                                                                                                                                                                                                                                                      1, if PTNi = RAWj
        2                                           binary relation extraction w Alignment Matrix
                                                                                                                                                                                                                                                   simi,j =              or PTNi = <label>
   more than 2                                      relation/event extraction         character
                                                                                                the character Michael Scofield portrayed by Wentworth Miller in the TV series Prison Break is
                                                                                                 0      1        1       1         1     1      1      1     1 1 1       1      1      1   1                                                                          0, otherwise
                                                                                                                               <ROLE>       1     1        2      2   2   2   2   2   2   2   2   2                       2     2   2

w Approaches                                                                                                                   portrayed
                                                                                                                                   by
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                                                                                                                                                                                                                                                w Trace Back
                                                                                                                              <ACTOR>       1     2        2      3   3   4   5   5   5   5   5   5                       5     5   5
  w Supervised                                                                                                                      in
                                                                                                                                   the
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                                                                                                                                                                                                                                                             M i,j                next position
  w Un/Semi-Supervised                                                                                                         television
                                                                                                                                 series
                                                                                                                                            1
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                                                                                                                                                                                                                                    8                   M i,j-1 +gp                  [i, j-1]
                                                                                                                                                                                                                                                       M i-1,j-1 + simi,j           [i-1, j-1]
                                                                                                                             <PROGRAM>      1     2        3      3   4   4   5   6   6   7   8   8                       9     9   9
                                                                                                                                    is      1     2        3      3   4   4   5   6   6   7   8   8                       9     9   10

                                                                                                                                                                                                                                                        M i-1,j +gp                  [i-1, j]

                                                                       Semi-supervised Relation Extraction Including Multiple Arguments
 v Overall Architecture                                                                                                               v Context Patterns Extraction                                                             v Alignment-based Verification
                                                                                                                                      1) Searching the sentences containing all                                                 w Aligning between two candidate arguments
                                                                                                                                      arguments of each tuple in source documents
                                                          Seed Data
                                                                                                                                      2) Segmenting out subpart of the sentence with                         max{M(A, B)}× 2
                                                                          n arguments
                                                                                                                                                                                         similarity(A,B) =
                                                                                                                                      the window size w                                                    length(A) + length(B)
                                                                                                                                      3) Replacing the parts of arguments in the sub-
   Seed Data    Seed Data              Seed Data           Seed Data   Seed Data          Seed Data         Seed Data
                                                                                                                                                                                      w Tuple clustering based on
                             2 arguments                                        k arguments                        n args
                                                                                                                                      sentence with argument labels
   Extracting   Extracting
                               …       Extracting
                                                      …   Extracting   Extracting
                                                                                    …     Extracting
                                                                                                       …    Extracting


                                                                                                                                      v Relation Extraction based on                    sim(tuple1, tuple2) =
    Context      Context                Context            Context      Context            Context           Context
    Patterns     Patterns               Patterns           Patterns     Patterns           Patterns          Patterns


    Relation     Relation               Relation           Relation     Relation           Relation          Relation
                                                                                                                                      Pairwise Alignment                                                                                        |args|
                                                                                                                                                                                                                                                                                     tuple2i)
                                                                                                                                                                                                                                                i=1 similarity(tuple1i,
   Extraction   Extraction             Extraction         Extraction   Extraction         Extraction        Extraction


                                                                                                                                      w Alignment score
                                                                                                                                                                                                                                                           |arguments|
                                           Validation &                                                                                                       max{M(PTN, RAW)}
                                            Integration
                                                                            Results
                                                                                                                                            score(PTN, RAW) =                                                                   w Selecting the most probable tuple for each
                                                                                              n arguments
                                                                                                                                                                 length(PTN)
                                                                                                                                                                                                                                cluster

                                                                                                                                                Experimental Results
v Experimental Setup
w 930 Korean news documents (13,175 sents) about TV series
w Only a tuple with 4 arguments (CHANNEL, PROGRAM, ACTOR, ROLE) is used as a seed
                                                                                                                                                                                                  v Comparison on the Coverage for
w Each result is collected after the first iteration and evaluated manually
                                                                                                                                                                                                  Various Threshold Values
v Result of the verification                                                                                   v Result of the integration
                                                                                                                                                                                                                         90

                                                                                                                                                                                                                         80

                                      before          after                                                                                       with only
    type of                                                                                                                                                              with all
                                                                                                                                                                                                                         70
                                  verification    verification                                                               type of               binary
   relations                                                                                                                                                          intermediates                                      60
                                |tuples|     P  |tuples|    P                                                               relations             relations
                                                                                                                                                                                                  # of correct results




    (A,R)                         249 36.55        79     73.42                                                                                 |tuples|         P   |tuples|   P
                                                                                                                                                                                                                         50

    (P,R)                          19     52.63    17     58.82                                                           (P,A,R)                   9          77.78     9    88.89                                      40

    (P,A)                          10       60     10       60                                                            (C,P,R)                  11          81.82    16    87.5                                       30
    (C,P)                          12     33.33     6     66.67                                                           (C,P,A)                  12          58.33     9    77.78                                      20
   (P,A,R)                          7     42.86     5       60                                                           (C,P,A,R)                  8          87.5     16    87.5                                                                                             including 2 arguments
   (C,P,R)                         18     55.56    16     81.25                                                                                                                                                          10                                                    including 3 arguments
                                                                                                                                                                                                                                                                               including 4 arguments
   (C,P,A)                          8      62.5     8       75                                                  w th = 0.85                                                                                              0
                                                                                                                                                                                                                         1.00            0.95   0.90         0.85       0.80           0.75            0.70
  (C,P,A,R)                        15       60     14     85.71                                                 w C(Channel), P(Program), A(Actor), R(Role)                                                                                               threshold

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An alignment-based approach to semi-supervised relation extraction including multiple arguments

  • 1. An alignment-based Approach to Semi-supervised Relation Extraction Including Multiple Arguments Seokhwan Kim, Minwoo Jeong, Gary Geunbae Lee, Kwangil Ko, and Zino Lee {megaup, stardust, gblee}@postech.ac.kr, {kik, zino}@alticast.com Abstract - We present an alignment-based approach to semi-supervised relation extraction task including more than two arguments. We concentrate on improving not only the precision of the extracted result, but also on the coverage of the method. Our relation extraction method is based on an alignment-based pattern matching approach which provides more flexibility of the method. In addition, we extract all relationships including two or more arguments at once in order to obtain the integrated result with high quality. We present experimental results which indicate the effectiveness of our method. Alignment-based Information Extraction v Information Extraction v Sentence Alignment for Information Extraction w Matrix Computation w Extracting the defined number of relevant w Example M i 1, j 1 sim i arguments from natural language documents the character <ROLE> portrayed by <ACTOR> in the television series <PROGRAM> is 1, j 1 M i 1, j gp w Subtasks M i, j max M i , j 1 gp # of arguments subtask 0 1 named-entity recognition character Michael Scofield portrayed by Wentworth Miller in the TV series Prison Break is { 1, if PTNi = RAWj 2 binary relation extraction w Alignment Matrix simi,j = or PTNi = <label> more than 2 relation/event extraction character the character Michael Scofield portrayed by Wentworth Miller in the TV series Prison Break is 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0, otherwise <ROLE> 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 w Approaches portrayed by 1 1 1 1 2 2 2 2 3 3 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 w Trace Back <ACTOR> 1 2 2 3 3 4 5 5 5 5 5 5 5 5 5 w Supervised in the 1 1 2 2 2 2 3 3 3 3 4 4 5 5 5 5 6 6 6 7 6 7 6 7 6 7 6 7 6 7 M i,j next position w Un/Semi-Supervised television series 1 1 2 2 2 2 3 3 3 3 4 4 5 5 5 5 6 6 7 7 7 7 7 8 7 8 7 8 7 8 M i,j-1 +gp [i, j-1] M i-1,j-1 + simi,j [i-1, j-1] <PROGRAM> 1 2 3 3 4 4 5 6 6 7 8 8 9 9 9 is 1 2 3 3 4 4 5 6 6 7 8 8 9 9 10 M i-1,j +gp [i-1, j] Semi-supervised Relation Extraction Including Multiple Arguments v Overall Architecture v Context Patterns Extraction v Alignment-based Verification 1) Searching the sentences containing all w Aligning between two candidate arguments arguments of each tuple in source documents Seed Data 2) Segmenting out subpart of the sentence with max{M(A, B)}× 2 n arguments similarity(A,B) = the window size w length(A) + length(B) 3) Replacing the parts of arguments in the sub- Seed Data Seed Data Seed Data Seed Data Seed Data Seed Data Seed Data w Tuple clustering based on 2 arguments k arguments n args sentence with argument labels Extracting Extracting … Extracting … Extracting Extracting … Extracting … Extracting v Relation Extraction based on sim(tuple1, tuple2) = Context Context Context Context Context Context Context Patterns Patterns Patterns Patterns Patterns Patterns Patterns Relation Relation Relation Relation Relation Relation Relation Pairwise Alignment |args| tuple2i) i=1 similarity(tuple1i, Extraction Extraction Extraction Extraction Extraction Extraction Extraction w Alignment score |arguments| Validation & max{M(PTN, RAW)} Integration Results score(PTN, RAW) = w Selecting the most probable tuple for each n arguments length(PTN) cluster Experimental Results v Experimental Setup w 930 Korean news documents (13,175 sents) about TV series w Only a tuple with 4 arguments (CHANNEL, PROGRAM, ACTOR, ROLE) is used as a seed v Comparison on the Coverage for w Each result is collected after the first iteration and evaluated manually Various Threshold Values v Result of the verification v Result of the integration 90 80 before after with only type of with all 70 verification verification type of binary relations intermediates 60 |tuples| P |tuples| P relations relations # of correct results (A,R) 249 36.55 79 73.42 |tuples| P |tuples| P 50 (P,R) 19 52.63 17 58.82 (P,A,R) 9 77.78 9 88.89 40 (P,A) 10 60 10 60 (C,P,R) 11 81.82 16 87.5 30 (C,P) 12 33.33 6 66.67 (C,P,A) 12 58.33 9 77.78 20 (P,A,R) 7 42.86 5 60 (C,P,A,R) 8 87.5 16 87.5 including 2 arguments (C,P,R) 18 55.56 16 81.25 10 including 3 arguments including 4 arguments (C,P,A) 8 62.5 8 75 w th = 0.85 0 1.00 0.95 0.90 0.85 0.80 0.75 0.70 (C,P,A,R) 15 60 14 85.71 w C(Channel), P(Program), A(Actor), R(Role) threshold