Entry Iwakura:2013:NER from talip.bib
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BibTeX entry
@Article{Iwakura:2013:NER,
author = "Tomoya Iwakura and Hiroya Takamura and Manabu
Okumura",
title = "A Named Entity Recognition Method Based on
Decomposition and Concatenation of Word Chunks",
journal = j-TALIP,
volume = "12",
number = "3",
pages = "10:1--10:??",
month = aug,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2499955.2499958",
ISSN = "1530-0226 (print), 1558-3430 (electronic)",
ISSN-L = "1530-0226",
bibdate = "Mon Aug 19 18:39:55 MDT 2013",
bibsource = "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/talip.bib",
abstract = "We propose a named entity (NE) recognition method in
which word chunks are repeatedly decomposed and
concatenated. Our method identifies word chunks with a
base chunker, such as a noun phrase chunker, and then
recognizes NEs from the recognized word chunk
sequences. By using word chunks, we can obtain features
that cannot be obtained in word-sequence-based
recognition methods, such as the first word of a word
chunk, the last word of a word chunk, and so on.
However, each word chunk may include a part of an NE or
multiple NEs. To solve this problem, we use the
following operators: SHIFT for separating the first
word from a word chunk, POP for separating the last
word from a word chunk, JOIN for concatenating two word
chunks, and REDUCE for assigning an NE label to a word
chunk. We evaluate our method on a Japanese NE
recognition dataset that includes about 200,000
annotations of 191 types of NEs from over 8,500 news
articles. The experimental results show that the
training and processing speeds of our method are faster
than those of a linear-chain structured perceptron and
a semi-Markov perceptron, while maintaining high
accuracy.",
acknowledgement = ack-nhfb,
articleno = "10",
fjournal = "ACM Transactions on Asian Language Information
Processing",
journal-URL = "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}
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6(3)10,
7(3)8,
7(4)11,
7(4)13,
8(2)8,
8(4)16,
9(1)2,
9(2)7,
9(3)11,
9(3)12,
10(1)3,
10(1)4,
10(3)14,
11(2)4,
11(2)7,
11(3)8,
11(3)9,
11(4)13,
11(4)15,
12(2)7,
12(4)16,
13(1)1,
13(4)17
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5(2)121,
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7(1)1,
7(3)10,
7(4)12,
8(1)4,
8(2)7,
8(3)10,
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8(3)12,
8(4)14,
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9(2)7,
9(3)11,
10(2)7,
10(3)13,
10(4)20,
11(1)3,
11(2)7,
11(3)11,
11(4)15,
12(1)3,
12(4)17,
13(1)4,
13(2)9,
13(3)13,
13(4)16
- training,
5(2)121,
6(3)11,
7(1)1,
8(1)3,
8(2)6,
8(2)7,
8(3)10,
9(2)5,
10(3)12,
10(3)13,
11(3)9,
12(1)1,
12(2)5,
12(3)9,
13(1)2,
13(1)3,
13(1)4,
13(4)17
- two,
5(2)89,
7(2)7,
7(3)8,
7(4)11,
7(4)12,
7(4)13,
8(1)4,
8(2)7,
8(4)17,
9(1)2,
9(3)11,
9(4)13,
10(1)2,
10(3)12,
10(3)14,
10(3)15,
10(4)20,
11(2)4,
11(2)5,
11(2)7,
11(3)8,
11(3)9,
11(3)11,
11(4)17,
12(1)1,
12(1)2,
12(1)4,
12(2)5,
12(3)11,
12(4)16,
13(1)1,
13(1)3,
13(1)4,
13(2)6,
13(2)9,
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13(4)17
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7(4)11,
8(4)17,
9(2)6,
9(3)11,
9(3)12,
10(4)19,
11(1)2,
11(1)3,
11(4)18,
12(3)11,
13(1)3,
13(2)6,
13(3)11
- use,
4(2)159,
5(2)89,
5(2)146,
6(2)8,
6(3)11,
7(2)6,
7(3)9,
7(4)11,
7(4)12,
8(1)3,
8(2)9,
8(3)10,
8(3)11,
9(1)1,
9(1)3,
9(3)11,
10(1)3,
10(1)4,
11(1)1,
11(2)6,
11(2)7,
11(3)8,
11(3)10,
11(4)14,
11(4)18,
12(1)1,
12(2)6,
12(3)9,
13(2)6,
13(2)9,
13(2)10,
13(3)12
- which,
5(2)89,
5(2)121,
6(2)6,
6(2)8,
6(3)10,
6(4)1,
7(1)3,
7(2)7,
7(3)10,
7(4)13,
8(1)2,
8(1)4,
8(2)9,
8(3)10,
8(3)12,
8(4)14,
8(4)16,
8(4)18,
9(1)1,
9(2)5,
9(3)12,
9(4)14,
9(4)15,
10(1)4,
10(1)6,
10(2)7,
10(2)8,
10(2)9,
10(3)12,
10(3)13,
10(3)14,
10(3)15,
10(4)17,
10(4)19,
10(4)20,
11(2)6,
11(3)8,
11(3)9,
11(4)13,
11(4)14,
11(4)15,
11(4)16,
11(4)18,
12(1)1,
12(1)3,
12(1)4,
12(2)5,
12(2)6,
12(2)7,
12(3)11,
12(4)15,
12(4)17,
13(1)1,
13(2)8,
13(3)13,
13(4)17,
13(4)18
- while,
5(2)165,
8(1)2,
8(4)18,
9(4)15,
10(1)4,
10(3)15,
11(2)4,
11(2)5,
12(3)11,
13(1)1,
13(2)8,
13(3)12,
13(3)14