U.S. patent application number 11/511750 was filed with the patent office on 2007-03-29 for corpus expansion system and method thereof.
This patent application is currently assigned to International Business Machines Corporation. Invention is credited to Hong Lei Guo, Zhi Li Guo, Zhao Ming Qiu, Li Qin Shen, Li Zhang.
Application Number | 20070073534 11/511750 |
Document ID | / |
Family ID | 37895261 |
Filed Date | 2007-03-29 |
United States Patent
Application |
20070073534 |
Kind Code |
A1 |
Guo; Hong Lei ; et
al. |
March 29, 2007 |
Corpus expansion system and method thereof
Abstract
A system and method for expanding new sample seeds to
automatically expand corpora, in which sample seeds are used to
collect corpus is provided. The new sample seeds are generated
based on the already existed sample seeds and collected corpora;
The corpus expansion strategy is determined based on all the sample
seeds having been used and new sample seeds: The new sample seeds
are refmed based on the corpus expansion strategy, and the refmed
new sample seeds are used to further collect corpus. The above
steps are repeatedly executed until predefined condition is
satisfied. According to the invention, corpus may be automatically
expanded from the web or other resources with low cost and in
convenient way to improve the coverage of corpora.
Inventors: |
Guo; Hong Lei; (Sun Garden,
CN) ; Zhang; Li; (Beijing, CN) ; Qiu; Zhao
Ming; (Beijing, CN) ; Shen; Li Qin; (Beijing,
CN) ; Guo; Zhi Li; (Beijing, CN) |
Correspondence
Address: |
SCULLY, SCOTT, MURPHY & PRESSER, P.C.
400 GARDEN CITY PLAZA
SUITE 300
GARDEN CITY
NY
11530
US
|
Assignee: |
International Business Machines
Corporation
Armonk
NY
|
Family ID: |
37895261 |
Appl. No.: |
11/511750 |
Filed: |
August 29, 2006 |
Current U.S.
Class: |
704/9 ;
707/E17.098 |
Current CPC
Class: |
G06F 16/36 20190101;
G06F 40/216 20200101 |
Class at
Publication: |
704/009 |
International
Class: |
G06F 17/27 20060101
G06F017/27 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 29, 2005 |
CN |
200510108065.9 |
Claims
1. A corpus expansion system, comprising: a corpus collection unit,
for collecting corpus using sample seeds; a sample seed expansion
unit, for generating new sample seeds, based on the sample seeds
having been used by the corpus collection unit and the collected
corpora; a balancing unit, for determining a corpus expansion
strategy based on all the sample seeds having been used by the
corpus collection unit and the new sample seeds; and a refining
unit, for refining the new sample seeds based on the corpus
expansion strategy.
2. The corpus expansion system according to claim 1, further
comprising a sample seed storing unit for storing all the sample
seeds having been used by the corpus collection unit, wherein said
corpus collection unit uses the refined new sample seeds to collect
corpus and said refining unit adds the refmed new sample seeds into
the sample seed storing unit.
3. The corpus expansion system according to claim 1, wherein, said
corpus expansion strategy includes at least one of the information
about the class in specific field in which the sample seeds need to
be expanded and the information about which sample seed need to be
deleted in said new sample seeds, wherein said refining unit
refines the new sample seeds based on the information.
4. The corpus expansion system according to claim 1, wherein, said
sample seed expansion unit comprises at least one of: a linguistic
structure analysis based sample seed expansion unit for generating
new sample seeds based on linguistic structure features and a seed
annotator based sample seed expansion unit for generating new
sample seeds based on known seed annotator.
5. The corpus expansion system according to claim 4, wherein,
further comprising a corpus storing unit for storing the corpora
collected by the corpus collection unit.
6. The corpus expansion system according to claim 4, wherein, the
corpus collection unit stops collecting corpus when a predefined
condition reaches.
7. The corpus expansion system according to claim 5, wherein, the
predefined condition is when the number of corpora reaches a
selected value, or when the ratio of the number of new sample seeds
to the number of the sample seeds when expanding corpus last time
is less than a specific value, or combination thereof.
8. A corpus expansion method, comprising: collecting corpus using
sample seeds; generating new sample seeds, based on the sample
seeds having been used and the collected corpora; determining a
corpus expansion strategy based on all the sample seeds having been
used and the new sample seeds; refining the new sample seeds based
on the corpus expansion strategy; and repeating the above steps
until a predefined condition reaches.
9. The corpus expansion method according to claim 8, further
including storing all the sample seeds having been used when
collecting corpus.
10. The corpus expansion method according to claim 8, wherein, the
step of generating new sample seeds comprises at least one of
generating new sample seeds based on linguistic structure features
and generating new sample seeds based on known seed annotator.
11. The corpus expansion method according to claim 10, further
comprising storing the collected corpora.
12. The corpus expansion method according to claim 11, wherein, the
predefined condition is when the number of corpora reaches a
certain value, or when the ratio of the number of new sample seeds
to the number of the sample seeds when expanding corpus last time
is less than a specific value.
13. The corpus expansion method according to claim 8, wherein, said
corpus expansion strategy includes at least one of the information
about the class in specific field in which the sample seeds need to
be expanded and the information about which sample seed need to be
deleted in said new sample seeds, wherein said reforming step
refines the new sample seeds based on the information.
14. A program product for realizing the method as described in
claim 8.
15. A storage medium for recording the program in claim 14.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to the field of information
extraction, knowledge mining and other natural language processing
applications, especially to a corpus expansion system and method
for expanding corpus based on which the machine learning method is
executed.
BACKGROUND OF THE INVENTION
[0002] Typically, the corpora collected manually or automatically
are analyzed with the machine learning method to generate the
classifier models of a certain specific class to be used in
information extraction, knowledge mining and other natural language
processing applications.
[0003] In the task-oriented or domain-oriented natural language
processing applications, such as domain-specific information
extraction and named entity recognition, collecting corpora with
extensive coverage and tagging the collected corpora are the
important factors for improving the recognition accuracy.
[0004] There exist some methods for automatically collecting and
tagging corpus. In these methods, corpora are collected from the
web or other resources by a search engine based on some sample
seeds. However, in these existing methods, the corpus coverage is
completely dependent on the limited initial sample seeds.
Therefore, it is required to collect richer corpus based on more
sample seeds.
SUMMARY OF THE INVENTION
[0005] To solve the problems mentioned above in the prior art, an
object of the invention is to provide a system and method for
automatically expanding corpus by expanding new sample seeds.
[0006] To obtain the above object, the present invention provides a
corpus expansion system, comprising: a corpus collection unit, for
collecting corpus using sample seeds; a sample seed expansion unit,
for generating new sample seeds, based on the sample seeds having
been used by the corpus collection unit and the collected corpora;
a balancing unit, for determining a corpus expansion strategy based
on all the sample seeds having been used by the corpus collection
unit and the new sample seeds; a refining unit, for refining the
new sample seeds based on the corpus expansion strategy.
[0007] The present invention further provides a corpus expansion
method, comprising: collecting corpus using sample seeds;
generating new sample seeds, based on the sample seeds having been
used and the collected corpora; determining a corpus expansion
strategy based on all the sample seeds having been used and the new
sample seeds; refining the new sample seeds based on the corpus
expansion strategy; repeating the above steps until a predefined
condition reaches.
[0008] According to the invention, corpus can be automatically
expanded from the web or other resources with low cost and in
convenient way.
[0009] According to the invention, the coverage of corpus may be
improved.
[0010] According to the invention, the quality of pre-tagging
corpus may be improved by using the sample seeds automatically
expanded in building the annotated corpus.
[0011] These and other advantages of the invention will be more
apparent by the detailed description of the preferred embodiments
of the invention, with reference to the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 shows the schematic block of the corpus expansion
system according to the first embodiment of the invention.
[0013] FIG. 2 shows the general flow chart of the corpus expansion
process according to the first embodiment of the invention.
[0014] FIG. 3 shows the structural block of the sample seed
expansion unit according to the first embodiment of the
invention.
[0015] FIG. 4 shows the flow chart of the sample seed expansion
process according to the first embodiment of the invention.
[0016] FIG. 5 shows the flow chart of the sample seed expansion
process based on linguistic structure analysis according to the
first embodiment of the invention.
[0017] FIG. 6 shows the flow chart of the sample seed expansion
process based on the available seed annotators according to the
first embodiment of the invention.
[0018] FIG. 7 shows the flow chart of determining whether to expand
sample seeds for a class according to the first embodiment of the
invention.
[0019] FIG. 8 shows the flow chart of determining the candidate
sample seeds need to be deleted according to the first embodiment
of the invention.
[0020] FIG. 9 shows the flow chart of refining the sample seed
according to the first embodiment of the invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0021] The method and system according to the invention will be
described with the named entity recognition as example. However, it
will be apparent for the persons in the art that the method and
system according to the invention can be applied to other similar
fields such as nominal entity recognition, relationship recognition
and information extraction.
[0022] The terms used in the invention will be explained first.
[0023] Specific field: the particular field where corpus is
collected, such as financial field, sports field, and entertainment
field. Named entity class under specific field (hereinafter named
as class): the class having practical meaning defined under the
specific field when collecting corpus for the specific field. For
example, the classes under the banking field are classified as bank
name class, representative name class, city name class.
[0024] Named entity: a word sequence representing an entity name
that has the practical meaning in each of the classes under
specific class, such as specific bank name, person's name, place
name.
[0025] Sample seed: the sample of named entity in specific field,
for example, certain bank name, person's name, place name. the
sample seed is used for searching resources when collecting
corpus.
[0026] Corpus: a segment in a natural language including one or
more sample seeds, such as an article or a snippet.
[0027] Raw corpus: corpus having not any annotation.
[0028] Corpus collection condition: the condition required when
collecting corpus. Only the corpus satisfying the condition can be
collected. For example, the condition includes the class such as
the financial class, the sports class or other class; the document
type such as WORD file, PDF file or other types; the language used
by the corpus; the maximum number of the collected corpora; whether
a corpus is a snippet or the whole document; the URL limitation on
corpus.
[0029] Sample seed list: similar sample seeds in each of the
classes under specific field are associatively stored. For example,
several specific bank names in the bank class under financial field
are similar sample seeds. These sample seeds compose one sample
seed list with each record including the sample seed and the class
the sample seed belongs to. According to the invention, the sample
seed list consists of the sample seeds used when initially
collecting corpus and the new sample seeds expanded according to
the invention. That is, the sample seed list includes all the
sample seeds ever used when collecting corpus.
[0030] New candidate sample seed list: the list including new
sample seeds expanded according to the invention that have not been
balanced and refined.
[0031] New sample seed list: the list including new sample seeds
expanded according to the invention that have been balanced and
refined. According to the invention, the new sample seeds in the
new sample seed list are used for the resource searching when
collecting corpus.
[0032] Corpus expansion strategy: the strategy used when expanding
corpus for the corpora currently collected. For example, the
strategy is the information about in which class under which field
corpus need to be expanded; or the strategy is the information
about which sample seed(s) need to be deleted from the collected
new sample seeds.
[0033] The following description is based on the following
assumption: corpus and the sample seeds are expanded for a certain
field.
[0034] FIG. 1 shows the schematic block of the corpus expansion
system according to the first embodiment of the invention. The
corpus expansion system according to the invention in one
embodiment comprises the corpus expansion apparatus 1 and the
storage apparatus 2. The corpus expansion apparatus 1 comprises the
corpus collection unit 101, the sample seed expansion unit 110, the
balancing unit 120, the refining unit 130 and the determining unit
145. The storage apparatus 2 comprises the raw corpus storing unit
105, the new candidate sample seed list storing unit 115, the new
sample seed list storing unit 125, the corpus expansion strategy
storing unit 135, the corpus collection condition storing unit 140
and the sample seed list storing unit 155. The raw corpus storing
unit 105 stores all the raw corpora collected by the corpus
collection unit 101, that is, the corpora having not any
annotation. The new candidate sample seed list storing unit 115
stores the new candidate sample seed list generated by the sample
seed expansion unit 110.
[0035] The new sample seed list storing unit 125 stores the list of
the new sample seeds obtained after the refining unit 130 refines
the new candidate sample seeds. When the system is initiated, the
new sample seed list storing unit 125 stores the sample seeds used
when the corpus is collected at the first time. The corpus
expansion strategy storing unit 135 stores the corpus expansion
strategy generated by the balancing unit 120.
[0036] The corpus collection condition storing unit 140 stores the
corpus collection condition. During the system initiation and the
following corpus expansion operation, the corpus collection
condition used in collecting corpus can be input manually. The
sample seed list storing unit 155 stores the list of all the sample
seeds expanded according to the invention. When the system is
initiated, the sample seed list storing unit stores the sample
seeds when corpus is initially collected. Every time the new sample
seed is expanded, it is added to the sample seed list storing unit
155.
[0037] The function of the blocks of the corpus expansion system
will be described by describing the corpus expansion process
according to the invention. FIG. 2 shows the general flow chart of
the corpus expansion process according to the invention. Firstly,
in step S201, the corpus collection unit 101 collects the initial
corpora from the web or other external resources by using known
art, based on the initial sample seeds stored in the new sample
seed list storing unit 125 and the corpus collection condition
stored in the corpus collection condition storing unit 140, and
stores the corpora into the raw corpus storing unit 105. In the
collected raw corpora, the sample seeds are not tagged.
[0038] The method for collecting corpus may be as follows: the new
sample seeds are used as the search keywords and submitted to the
web search engine or other search system of external resource; the
results are obtained after searching which include the storing
address (e.g., URL) of the natural language snippets or articles
including the keywords; the results are filtered based on the
corpus collection condition; the natural language snippets or
articles satisfying the condition are downloaded to the local
system and stored in the raw corpus storing unit 105.
[0039] In step S205, the sample seed expansion unit 110 extracts
the new candidate sample seeds from all the currently available raw
corpora stored in the raw corpus storing unit 105, based on the
later described method, and associates the extracted new candidate
sample seeds with the corresponding class of specific field to
generate a list and stores the list into the new candidate sample
seed list storing unit 115.
[0040] In step S210, the balancing unit 120 analyzes the
distribution of the sample seeds stored in the sample seed list
storing unit 155 and the new candidate sample seed list storing
unit 125, to generate the corpus expansion strategy, and stores the
strategy into the corpus expansion strategy unit 135.
[0041] In step S215, the refining unit 130 refmes the candidate
sample seeds stored in the new candidate sample seed list storing
unit 115, based on the corpus expansion strategy stored in the
corpus expansion strategy storing unit 135, to generate the new
sample seed list, stores the list into the new sample seed list
storing unit 125, and adds the new sample seeds into the sample
seed list storing unit 155.
[0042] In step S220, the determining unit 145 determines whether
the predefined corpus collection end condition is satisfied. When
the condition is satisfied, it proceeds to the step S230 where the
corpus collection unit stops the expansion of corpus. Otherwise, it
proceeds to the step S225. The predefined condition, for example,
is when the sample seed expansion unit 110 does not expand any new
sample seeds, or when the ratio of the number of new sample seeds
to the number of the sample seeds in expanding corpus last time is
less than a specific value, or when the number of the raw corpora
stored in the raw corpus storing unit 105 reaches to a certain
value.
[0043] In step S225, the corpus collection unit 101 collects the
new corpora based on the expanded new sample seeds stored in the
new sample seed list storing unit 125 and the corpus collection
condition stored in the corpus collection condition storing unit
140, combines the collected new corpora and the raw corpora stored
in the corpus storing unit 105 and stores the combined ones into
the corpus storing unit 105.
[0044] And then it returns to the step S205 to continue the
process.
[0045] The process of the sample seed expansion unit 110 will be
described now.
[0046] FIG. 3 shows the structure of the sample seed expansion unit
110.
[0047] In FIG. 3, the parts which are the same as FIG. 1 have the
same reference numerals as FIG. 1. The sample seed expansion unit
110 comprises the linguistic structure analysis based sample seed
expansion unit 301, the seed annotator based sample seed expansion
unit 305 and the new candidate sample seed combination unit
310.
[0048] FIG. 4 shows the process of sample seed expansion according
to the sample seed expansion unit 110 of the invention. In step
401, the linguistic structure analysis based sample seed expansion
unit 301 expands to generate new candidate sample seeds, and
provides the generated new candidate sample seeds to the new
candidate sample seed combination unit 310. In step 405, the seed
annotator based sample seed expansion unit 305 expands to generate
new candidate sample seeds, and provides the generated new
candidate sample seeds to the new candidate sample seed combination
unit 310. In step S410, the new candidate sample seed combination
unit 310 combines the new candidate sample seeds generated in the
step S401 and the step S405 to generate the new candidate sample
seed list and store the new candidate sample seed list into the new
candidate sample seed list storing unit 115.
[0049] Taking a raw corpus as an example, the process procedure of
the linguistic structure analysis based sample seed expansion unit
301 will be described now.
[0050] In FIG. 5, in step S501, the initial sample seeds are tagged
by tags for all the collected raw corpora having not been tagged in
the raw corpus storing unit 105. The initial sample seeds are the
sample seeds stored in the sample seed list storing unit 155. The
tagged corpora are stored in a storing unit not shown.
[0051] In step S505, all the coordinate syntactic structures which
contain the initial sample seeds are extracted from the tagged
corpora using the known linguistic rule database. For example, the
coordinate syntactic structures with the conjunction words such as
"and", "such as", "etc." or punctuations are extracted from the
sentences in the corpora based on the coordinate syntactic rules.
The coordinate syntactic structure, for example, is a snippet
including initial sample seed(s) and some content on the left and
the right of the initial sample seed(s).
[0052] In Step S510, all the new candidate sample seeds are
extracted from the extracted coordinate syntactic structures. In
step S515, the new candidate sample seeds extracted from the tagged
corpora are further tagged by using the same tags as that of the
initial sample seeds in the extracted coordinate syntactic
structures. In step S520, the extracted new candidate sample seeds
are provided to the new candidate sample seed combination unit
310.
[0053] The process procedure of the seed annotator based sample
seed expansion unit 305 will be described now. As shown in FIG. 6,
in step S601, all the raw corpora collected by the corpora
collection unit 101 are automatically tagged by using a seed
annotator to tag the sample seeds. The tagged corpora are stored in
a storing unit not shown.
[0054] The seed annotation may be an independent known means which
can automatically tag entities appeared in a raw corpus, or be a
seed annotator obtained through machine learning based on the
currently collected corpora. In the later case, the step S601
executes the following process: firstly, the initial sample seeds
in the raw corpora not tagged in the raw corpus storing unit 105
are tagged by tags, in which the initial sample seeds are the
sample seeds stored in the sample seed list storing unit 155; then,
the tagged corpora are supplied to the known machine learning
module to train a seed annotator; lastly, the entity annotator
obtained after training is used to recognize the raw corpora to get
the corpora being tagged.
[0055] In step S605, the tagged sample seeds are extracted from the
automatically tagged corpora. In step S610, the sample seeds except
those in the sample seed list storing unit 155 are extracted from
the extracted tagged sample seeds, as the new candidate sample
seeds. It will be apparent for the persons in the art that the
recognition result of the above seed annotator will be different
from the sample seeds stored in the sample seed list storing unit
155. In step S615, the extracted new candidate sample seeds are
provided to the new candidate sample seed combination unit 310. The
sample seeds may be effectively expanded through the process of the
above FIGS. 4 to 6.
[0056] The operations of refining the new candidate sample seeds
and refining the corpus expansion strategy according to the
invention will be described. The imbalanced corpus will bring many
difficulties to machine learning, such as decreasing the
performance and accuracy of the classification. The balancing unit
120 and the refining unit 130 are used to implement the sample seed
evaluation, and provide the corpus expansion strategy based on the
distribution of the sample seeds to refine the new sample
seeds.
[0057] FIG. 7 shows the flow chart of determining whether to expand
the sample seeds in a certain class according to the balancing unit
120 of the invention. Assuming there are N classes
{C.sub.1,C.sub.2, . . . ,C.sub.n} of entities; the number of the
sample seeds in these classes are {m.sub.1,m.sub.2, . . .
,m.sub.n}; the threshold M.sub.t represents the allowed minimum
sample seeds number in a certain class; r.sub.i
(1.ltoreq.i.ltoreq.n) represents the ratio of the number of the
sample seeds in each of the classes to the sum of the number of the
sample seeds in each of the other classes; the threshold R
represents the value that the ratio needs to reach.
[0058] Firstly, in step S700, the number {m.sub.1,m.sub.2, . . .
,m.sub.n} of the sample seeds in each of the classes
{C.sub.1,C.sub.2, . . . ,C.sub.n} in specific field are counted,
based on the sample seeds stored in the sample seed list storing
unit 155 and the new candidate sample seeds stored in the new
candidate sample seed list storing unit 115.
[0059] In step S701, i is set to 1.
[0060] In step S705, it is determined if m.sub.i.ltoreq.M.sub.t is
satisfied
[0061] When satisfied, it proceeds to the step S710, the
information about the sample seeds in C.sub.i class need to be
expanded is stored in the corpus expansion storing unit 135 as a
class expansion strategy.
[0062] When not satisfied, it proceeds to the step S715, setting r
i = m i j .di-elect cons. ( 1 , .times. .times. N ) .times. .times.
and .times. .times. j .noteq. i .times. m j ##EQU1##
[0063] In step S720, it determines whether the r.sub.i<R is
satisfied. When satisfied, it proceeds to the step S710, the
information about the sample seeds in C.sub.i class need to be
expanded is stored in the corpus expansion storing unit 135 as a
class expansion strategy.
[0064] When not satisfied, it proceeds to the step S725, setting
i=i+1.
[0065] In step S730, it determines whether the i.ltoreq.N is
satisfied. When satisfied, the process ends.
[0066] Otherwise, it returns to the step S705 to continue the
balancing process of the next class.
[0067] Through the above operation, the information about in which
class the sample seeds need to be expanded is stored in the corpus
expansion storing unit 135, for being used when refining the
candidate sample seeds.
[0068] However, the problem of the decreasing of the performance of
the machine learning can not be sufficiently solved by storing the
information about in which class the sample seeds need to be
expanded, because the new candidate sample seeds expanded in one
class may be overlapped with the sample seeds in other class in
feature space. Then the invention further refines the new candidate
sample seeds in the class which is needed to be expanded so as to
get the new sample seeds used when collecting corpus next time.
[0069] Assuming there are N classes {C.sub.1,C.sub.2, . . .
,C.sub.n} of entities; the number of the sample seeds in these
classes are {m.sub.1,m.sub.2, . . . ,m.sub.n}. And assuming it is
found the class C.sub.k need to be expanded through the method in
FIG. 7.
[0070] Through the process of FIG. 8, the information about which
candidate sample seeds need to be deleted is obtained. As shown in
FIG. 8, in step S801, i is set as 1.
[0071] In step S805, it is determined whether C.sub.i is not equal
to C.sub.k. When the two values equals to each other, it proceeds
to the step S804. Otherwise, it proceeds to the step S810.
[0072] In step S810, the centroid S.sub.i of the sample seeds in
the class C.sub.i stored in the sample seed list storing unit 155
is computed based on the formula: S i = 1 m i .times. x .di-elect
cons. C i .times. V x ##EQU2##
[0073] where, x represents each of the existed sample seeds in the
class C.sub.i, V.sub.x represents the feature vector of the sample
seed x. Then, in step S815, calculate the distance between each of
the sample seeds x and S.sub.i: d i = 1 cos .function. ( V x , S i
) ##EQU3##
[0074] In step S820, the maximum distance d.sub.max is obtained
based on the calculated d.sub.i, that is, d.sub.max=max
(d.sub.i)
[0075] In step S825, setting y is the new candidate sample seed in
the class C.sub.k, V.sub.y represents the feature vector value of
the sample seed y. The distance between y and S.sub.i is calculated
using d k = 1 cos .times. ( V y , S i ) ##EQU4##
[0076] In step S830, d.sub.k is compared to d.sub.max, when
d.sub.k<d.sub.max, it is determined y is the new candidate
sample need to be deleted from the new candidated sample seeds in
the class C.sub.k, and stores the information the new candidate
sample seed need to be deleted as a new sample seed expansion
strategy into the corpus expansion storing unit 135.
[0077] In step S835, it determined whether all the new candidate
sample seeds in the class C.sub.k are processed. If not processed,
it returns to the step S825. the step S825 and S830 are repeatedly
executed until all the new candidate sample seeds in the class
C.sub.k are processed.
[0078] When all the new candidate sample seeds are processed, it
proceeds to the step S840 toset i=i+1.
[0079] In step S845, it determines whether the i.ltoreq.N is
satisfied. When satisfied, it returns to the step S805 to continue
balance the new candidate sample seeds in the next class C.sub.i to
be expanded.
[0080] Otherwise, the process ends.
[0081] For the classes which need to be expanded stored in the
corpus expansion strategy storing unit 135 through the process in
FIG. 7, the process in FIG. 8 are respectively processed so as to
determine which new candidate sample seeds cannot be used as new
sample seeds for each of the class C.sub.k that need to be
expanded.
[0082] FIG. 9 shows the operation procedure of the refining unit
130 which refines the new sample seeds based on the new sample
seeds expansion strategy stored in the corpus expansion strategy
storing unit 135 according to the process of FIG. 7 and 8.
[0083] Firstly, in step S901, the refining unit 130 deletes the new
candidate sample seeds of the classes that does not need to be
expanded from the new candidate sample seed list storing unit,
based on the class expansion strategies stored in the corpus
expansion strategy storing unit 135, to generate the new candidate
sample seed list after class expansion. In step S905, the refminig
unit 130 deletes the new candidate sample seeds chosen in FIG. 8
from the new candidate sample seed list, to generate the new sample
seed list.
[0084] In step S910, the new sample seed list is stored in the new
sample seed list storing unit 125, the new sample seeds in the new
sample seed list is added into the sample seed list storing unit
155.
[0085] In addition, the contents in the new candidate sample seed
list storing unit 115 are deleted to store the new candidate sample
seeds to be used when collecting corpus next time. The contents in
the corpus expansion strategy storing unit 135 are deleted to be
used when generating the new corpus expansion strategies.
[0086] In addition, as to the new sample seed list storing unit
125, the contents thereof are deleted each time after the corpus
collection unit 101 collects corpora.
[0087] In addition, as to the contents in the corpus collection
condition storing unit 140, an input device not shown may be used
to manually adjust which corpus collection condition could be
remained to be used when collecting corpus, each time before the
corpus collection unit 101 collects corpus.
[0088] Through the above processes, both the sample seeds and
corpora are expanded.
Other Embodiments
[0089] In the above embodiment, the (new) candidate sample seed
list storing unit and the corpus expansion strategy storing unit
are used as temporal storing unit to store the temporal data. It
will be apparent for the persons in the art to omit these temporal
storing means by directly transferring the temporal data from
previous processing unit to the corresponding next processing unit.
In addition, The expanded sample seeds stored in the sample seed
list storing unit 155 are used in information extraction, knowledge
mining or other natural language processing applications.
[0090] Further, the expanded sample seeds stored in the sample seed
list storing unit 155 may be used by known sample seed annotator to
pre-tag the raw corpora stored in the raw corpora storing unit 105,
or pre-tag the corpora from other sources, so as to improve the
annotation performance of pre-tagging. Further, the tagged corpora
may be stored for using in information extraction, knowledge mining
or other natural language processing applications.
[0091] In addition, the corpus expansion apparatus according to the
invention can be implemented by running a program by a processor,
or by a logic circuit, or the combination of program and logic
circuit. In addition, the storing unit according to the invention
can be implemented by a readable RAM or any other known storage
means.
[0092] In addition, the above operation process can be implemented
in the manner of program executable by computer stored in different
medium, or by being download from communication network and being
executed by computer. The medium for providing the program
comprises for example, memory or memory unit, semiconductor, disk
unit such as optical disk, magnetic disk and magneto-optic disk or
any other medium suitable for storing information.
[0093] The present invention may have a plurality of varied
embodiments within the spirit and scope of the invention.
Therefore, it will be appreciated that, the scope of protection of
the invention is defined by the following claims, but not limited
to the specific embodiments.
[0094] While the embodiments of the invention have been described
in detail with reference to attached drawings, various changes and
modifications may be made to the above embodiments without
departing from the spirit and scope of the invention. Therefore,
the scope of the invention is only defined by the attached
claims.
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