CN114579675A - Data processing system for determining common finger event - Google Patents

Data processing system for determining common finger event Download PDF

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CN114579675A
CN114579675A CN202210480177.0A CN202210480177A CN114579675A CN 114579675 A CN114579675 A CN 114579675A CN 202210480177 A CN202210480177 A CN 202210480177A CN 114579675 A CN114579675 A CN 114579675A
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entity
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CN114579675B (en
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唐亚萍
常鸿宇
傅晓航
林方
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Zhongke Yuchen Technology Co Ltd
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    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract

The invention relates to a data processing system for determining co-referred events. The system comprises: a database, a processor and a memory storing a computer program which, when executed by the processor, performs the steps of: acquiring a first entity list corresponding to a first event and a second entity list corresponding to a second event, clustering the first entity list to obtain a first entity cluster list, clustering the second entity list to obtain a second entity cluster list, acquiring the same number of entity clusters and event similarity between the first event and the second event according to the first entity cluster list and the second entity cluster list, and determining whether the first event and the second event are the same event according to the event similarity; the method and the device can accurately determine that the two events represent the same event, avoid repeated query of the user and improve the experience of the user, and avoid the repeated text describing the same event and reduce the text amount of the event.

Description

Data processing system for determining common-finger events
Technical Field
The invention relates to the technical field of event processing, in particular to a data processing system for determining a common finger event.
Background
Currently, each event is independent, but in practice, there is often an association relationship between two events, or two events are to be referred to as representing the same event, and only because the manner of describing the event is different, when the event is classified, the event is divided into two events.
Therefore, some problems may arise: for example, on one hand, two events are used for representing the same event, which causes the user to repeatedly query the same event, which affects the experience of the user, and on the other hand, because the two events represent the same event, the text corresponding to each event has repeated text, which causes a large amount of text.
Disclosure of Invention
In view of the above technical problems, the technical solution adopted by the present invention is a data processing system for determining a common denominator event, the system comprising: a database, a processor, and a memory storing a computer program, wherein the database comprises: text set of events H = { H = { (H)1，……，Hr，……，Hs}，HrRefers to a text list corresponding to the r-th event, r =1 … … s, s is the number of events, and when the computer program is executed by a processor, the following steps are implemented:
s100, traversing H and according to HrAcquiring a first entity list A = { A) corresponding to the first event1，……，Ai，……，AM}，AiThe method refers to the ith first entity corresponding to the first event, i =1 … … M, and M is the number of the first entities;
s200, according to HrObtaining a second entity list B = { B ] corresponding to the second event1，……，Bj，……，BN}，BjJ =1 … … N, where N is the number of second entities;
s300, for all AiClustering to obtain a first entity cluster list C = { C = { (C) }1，……，Cx，……，Cp}，CxThe method is characterized in that the method refers to an x-th first entity cluster, wherein x =1 … … p, and p is a first entity cluster;
s400, for all BjClustering to obtain a second entity cluster list D = { D =1，……，Dy，……，Dq}，DyThe method is characterized in that the method refers to the y-th second entity cluster, wherein y =1 … … q, and q is the second entity cluster;
s500, when CxAnd DyWhen they are consistent, determining CxThe corresponding first entity cluster is a target entity cluster;
s600, acquiring the number k of the target entity clusters and acquiring event similarity F between a first event and a second event based on k;
s700, when F is more than or equal to F0Determining that the first event and the second event refer to the same event, wherein F0The method comprises the steps of indicating a preset event similarity threshold value;
s800, when F is less than F0Determining that the first event and the second event do not refer to the same event.
Compared with the prior art, the invention has obvious advantages and beneficial effects. By means of the technical scheme, the data processing system for determining the common finger event can achieve considerable technical progress and practicability, has wide industrial utilization value and at least has the following advantages:
a data processing system for determining a co-referent event according to the present invention comprises: a database, a processor, and a memory storing a computer program, wherein the database comprises: a text set of events, which when executed by a processor, performs the steps of: acquiring a first entity list corresponding to a first event and a second entity list corresponding to a second event according to a text list of any event in a text set of the event, clustering all first entities in the first entity list to obtain a first entity cluster list, clustering all second entities in the second entity list to obtain a second entity cluster list, acquiring the same number of entity clusters in the first entity cluster list and the second entity cluster list according to the first entity cluster list and the second entity cluster list so as to acquire event similarity between the first event and the second event according to the same number of entity clusters, and determining whether the first event and the second event are the same event according to the event similarity; on one hand, the method can accurately determine that two events represent the same event, avoid repeated inquiry of a user and improve the experience of the user, and on the other hand, avoid the texts of repeated texts to describe the same event and reduce the text amount of the events.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
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FIG. 1 is a flow chart of a computer program executed by a data processing system for determining co-designated events according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given with reference to the accompanying drawings and preferred embodiments of a data processing system for acquiring a target position and its effects.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Examples
The present embodiment provides a data processing system for determining a co-referred event, the system comprising: database, processor and computer programA memory, wherein the database comprises: text set of events H = { H = { (H)1，……，Hr，……，Hs}，HrRefers to the text list corresponding to the r-th event, r =1 … … s, s is the number of events, and when the computer program is executed by a processor, the following steps are implemented, as shown in fig. 1:
s100, traversing H and according to HrAcquiring a first entity list A = { A) corresponding to the first event1，……，Ai，……，AM}，AiThe method refers to the ith first entity corresponding to the first event, i =1 … … M, and M is the number of the first entities.
Specifically, a is also acquired in the S100 step by:
s101, determining any H from HrTaking the corresponding event as a first event;
s103, obtaining
Figure 593395DEST_PATH_IMAGE002
，HraRefers to the a text of the r event, where a =1 … … er，erThe number of texts referring to the r-th event;
s105, for HraPerforming entity extraction to obtain HraA corresponding first initial entity list and a first intermediate entity list are obtained based on all the first initial entity lists, wherein a person skilled in the art can adopt any entity extraction method in the prior art, and details are not repeated herein;
s107, performing deduplication processing on the first intermediate entity list to obtain a, and a person skilled in the art may adopt any entity deduplication method, which is not described herein again.
S200, according to HrAcquiring a second entity list B = { B ] corresponding to the second event1，……，Bj，……，BN}，BjThe j =1 … … N is the number of the second entities corresponding to the second event.
Specifically, B is also acquired in the S200 step by:
s201, deleting H from HrAcquiring a corresponding text list to obtain a middle text set H ' = { H ' of the event '1，……，H't，……，H's-1}，H'tThe method is characterized by comprising the steps of referring to a text list corresponding to the t-th event in the middle text set of the event, wherein t =1 … … s-1;
s203, determining any H 'from H'tTaking the corresponding event as a second event;
s205, obtaining
Figure 743753DEST_PATH_IMAGE003
，H'tbRefers to the b-th text of the t-th event, where b =1 … … ft，ftThe number of texts of the t-th event is referred to;
s207, to H'tbCarrying out entity extraction to obtain H'tbA corresponding second initial entity list and a second intermediate entity list are obtained based on all the second initial entity lists, wherein a person skilled in the art can adopt any entity extraction method in the prior art, and details are not repeated herein;
s209, performing deduplication processing on the second intermediate entity list to obtain B, and a person skilled in the art may adopt any entity deduplication method, which is not described herein again.
S300, for all AiClustering to obtain a first entity cluster list C = { C = { (C) }1，……，Cx……，Cp}，CxRefers to the x-th first entity cluster, x =1 … … p, p is the first entity cluster.
Specifically, the step S300 further includes the steps of:
s301, obtaining a first ontology list A '= { A'1，……，A'α，……，A'm}，A'αRefers to the α -th first body corresponding to a, α =1 … … m, and m is the number of first bodies corresponding to a.
Specifically, a' is also acquired in the following steps before the step S301:
s3011, obtaining AiCorresponding body and based on all AiCorresponding ontology, constructing the first initial ontology list corresponding to A, and the skilled in the artThe body can be obtained by any method in the prior art, and the details are not repeated herein; the first initial ontology list includes a plurality of ontology types, e.g., places, people, etc.
S3013, perform deduplication processing on the first initial ontology list corresponding to the first entity to obtain a', where a person skilled in the art may obtain the first ontology list by using any deduplication method in the prior art, and details are not described here.
S303, obtaining A'αCorresponding entity list
Figure 322371DEST_PATH_IMAGE004
，UαgRefers to the g-th first entity in the entity list corresponding to the alpha-th first ontology, g =1 … …
Figure 583588DEST_PATH_IMAGE006
，
Figure 799937DEST_PATH_IMAGE008
Is of A'αThe number of the first entities in the corresponding entity list.
S305 according to UαgObtaining UαgCorresponding first Key entity List U'αg={U1 αg，……，Uβ αg，……，Uz αg}，Uβ αgIs beta to the UαgThe corresponding first key entity, β =1 … … z, z obeys the following condition:
Figure 793301DEST_PATH_IMAGE010
。
specifically, UαgThe corresponding first key entity is denoted at A'αDivide U in the corresponding entity listαgAny first entity other than.
S307, according to UαgAnd U'αgObtaining UαgCorresponding first entity similarity list Tαg={T1 αg，……，Tβ αg，……，TZ αg}，Tβ αgIs referred to as UαgAnd Uβ αgThe entity similarity between them.
In particular, Tβ αgThe following conditions are met:
Figure 344368DEST_PATH_IMAGE012
wherein, in the step (A),
Figure 192631DEST_PATH_IMAGE014
is referred to as UαgThe corresponding gamma bit value, NK, in the vectorβ γIs referred to as Uβ αgThe corresponding gamma bit value in the vector, gamma =1 … …
Figure 778334DEST_PATH_IMAGE016
，
Figure 755648DEST_PATH_IMAGE018
Is referred to as UαgVector dimension in corresponding vector and Uβ αgCorresponding vector and UαgThe vector dimensions of the corresponding vectors are consistent.
Preferably, UαgCorresponding vector sum Uβ αgThe corresponding vectors are obtained through a pre-training language model, such as a Bert model, a sensor-transformer model, and the like.
Further, UαgCorresponding vector sum Uβ αgThe corresponding vector is a 768-dimensional vector, i.e.
Figure 794011DEST_PATH_IMAGE020
=768。
S309 according to TαgAnd acquiring a first entity cluster.
Specifically, the step S309 further includes the steps of:
s3091, traverse TαgAnd when each T isβ αg＜F'0Determining UαgIs a first solid cluster block, wherein'0Refers to a predetermined entity similarity.
Preference is given toAnd F'0Is 0.6.
S3092, when any Tβ αg≥F'0From TαgThe first key entity corresponding to the maximum similarity is obtained as the first target entity and is based on the first target entity and the UαgBuilding UαgCorresponding first intermediate entity clusters.
S3093, according to the vector corresponding to the first target entity
Figure 69135DEST_PATH_IMAGE021
Figure 758611DEST_PATH_IMAGE023
And UαgCorresponding vector
Figure 952832DEST_PATH_IMAGE024
Figure DEST_PATH_IMAGE025
Obtaining UαgVectors of corresponding intermediate entity clusters
Figure DEST_PATH_IMAGE027
Figure DEST_PATH_IMAGE029
Wherein, in the step (A),
Figure DEST_PATH_IMAGE031
refers to the gamma bit value in the vector corresponding to the first target entity,
Figure 933951DEST_PATH_IMAGE032
means that
Figure 560235DEST_PATH_IMAGE033
The corresponding average value, wherein,
Figure 120530DEST_PATH_IMAGE035
the following conditions are met:
Figure 938182DEST_PATH_IMAGE037
。
s3094 according to UαgCorresponding intermediate solid clusters and UαgObtaining U corresponding to the first appointed entity listαgCorresponding first intermediate similarity list, wherein UαgAny one of the corresponding first intermediate similarity lists
Figure 888820DEST_PATH_IMAGE038
Corresponding intermediate degree of similarity means
Figure 833642DEST_PATH_IMAGE040
And any one UαgSimilarity between vectors of corresponding first designated entities.
Specifically, UαgThe corresponding first designated entity is U'αgAny first key entity other than the first target entity.
In particular, the amount of the solvent to be used,
Figure 999176DEST_PATH_IMAGE041
corresponding intermediate similarity obtaining method and Tβ αgThe obtaining methods are consistent and are not described herein again.
S3095, mixing UαgInserting the first designated entity corresponding to the maximum similarity obtained from the corresponding first intermediate similarity list into UαgGenerating new U in corresponding intermediate entity clusterαgCorresponding intermediate entity clusters.
S3096, repeating the steps S3093-S305 until UαgEach similarity in the corresponding similarity list is less than F'0While determining UαgTaking an intermediate entity cluster corresponding to the corresponding final similarity list as a first entity cluster; it can be understood that: different entities are clustered into an entity cluster, so that whether different events refer to the same event or not is determined according to the same number of the entity clusters among different events, and the experience of users is improved.
S400, for all BjClustering to obtain a second entity cluster list D = { D =1，……，Dy，……，Dq}，DyRefers to the y second entity cluster, wherein y =1 … … q, and q is the second entity cluster.
Specifically, the method for acquiring the second entity cluster is the same as the method for acquiring the first entity cluster, and is not described herein again.
S500, when CxAnd DyWhen they are consistent, determining CxThe corresponding first entity cluster is a target entity cluster;
specifically, the step S500 further comprises the following steps of target entity clusters:
s501, obtaining Cx={Cx1，……，Cxη，……，Cxn}，CxηThe method is characterized in that the method refers to an eta first entity in an x first entity cluster, eta =1 … … n, and n is the number of the first entities in the x first entity cluster;
s503, obtaining Dy={Dy1，……，Dyδ，……，DywThe Dy delta refers to a delta second entity in the y second entity cluster, delta =1 … … w, and w is the number of the second entities in the y second entity cluster;
s505, when Cxη=DyδWhen, C is determinedxAnd the corresponding first entity cluster is a target entity cluster.
S600, acquiring the number k of the target entity clusters and acquiring event similarity F between a first event and a second event based on k.
Specifically, F satisfies the following condition:
F=2k/（p+q）。
in a specific embodiment, F further satisfies the following condition:
F=k（p+q）/2pq。
s700, when F is more than or equal to F0When the first event and the second event refer to the same event, namely the first event and the second event are determined as the common events, wherein F0Refers to a preset event similarity threshold.
Specifically, the method further includes the following steps before the step S700:
when F =2 k/(p + q), F is determined0=F01Wherein F is01To prepareSetting a first similarity threshold;
when F = k (p + q)/2 pq, F is determined0=F02Wherein F is02Is a preset second similarity threshold.
In the above, different similarity thresholds are determined according to different acquisition modes of the similarity, so that the method can be accurate.
Further, F01≠F02Preferably, F01Has a value of 0.8, F02Is 0.85.
S800, when F is less than F0Determining that the first event and the second event do not refer to the same event.
The embodiment provides a data processing system for determining a common finger event, which comprises: a database, a processor, and a memory storing a computer program, wherein the database comprises: a text set of events, which when executed by a processor, performs the steps of: acquiring a first entity list corresponding to a first event and a second entity list corresponding to a second event according to a text list of any event in a text set of the event, clustering all first entities in the first entity list to obtain a first entity cluster list, clustering all second entities in the second entity list to obtain a second entity cluster list, acquiring the same number of entity clusters in the first entity cluster list and the second entity cluster list according to the first entity cluster list and the second entity cluster list so as to acquire event similarity between the first event and the second event according to the same number of entity clusters, and determining whether the first event and the second event refer to the same event or not according to the event similarity; the method has the advantages that the same event represented by the two events can be accurately determined, repeated query of a user is avoided, experience of the user is improved, the same event is prevented from being described by texts of repeated texts, and the text quantity of the events is reduced.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A data processing system for determining coreference events, the system comprising: a database, a processor, and a memory storing a computer program, wherein the database comprises: text set of events H = { H = { (H)1，……，Hr，……，Hs}，HrRefers to a text list corresponding to the r-th event, r =1 … … s, s is the number of events, and when the computer program is executed by a processor, the following steps are implemented:
s100, traversing H and according to HrAcquiring a first entity list A = { A) corresponding to the first event1，……，Ai，……，AM}，AiThe method refers to the ith first entity corresponding to the first event, i =1 … … M, and M is the number of the first entities;
s200, according to HrAcquiring a second entity list B = { B ] corresponding to the second event1，……，Bj，……，BN}，BjJ =1 … … N, where N is the number of second entities;
s300, for all AiClustering to obtain a first entity cluster list C = { C = { (C) }1，……，Cx，……，Cp}，CxThe method is characterized in that the method refers to an x-th first entity cluster, wherein x =1 … … p, and p is a first entity cluster;
s400, for all BjClustering to obtain a second entity cluster list D = { D =1，……，Dy，……，Dq}，DyThe method is characterized in that the method refers to the y-th second entity cluster, wherein y =1 … … q, and q is the second entity cluster;
s500, when CxAnd DyWhen they are consistent, determine CxThe corresponding first entity cluster is a target entity cluster;
s600, acquiring the number k of the target entity clusters and acquiring event similarity F between a first event and a second event based on k;
s700, when F is more than or equal to F0Determining that the first event and the second event refer to the same event, wherein F0The method comprises the steps of indicating a preset event similarity threshold value;
s800, when F is less than F0Determining that the first event and the second event do not refer to the same event.
2. The data processing system for determining co-designated events according to claim 1, characterized in that a is also obtained in step S100 by:
s101, determining any H from HrTaking the corresponding event as a first event;
s103, obtaining
Figure 975401DEST_PATH_IMAGE002
，HraRefers to the a text of the r event, where a =1 … … er，erThe number of texts referring to the r-th event;
s105, for HraPerforming entity extraction to obtain HraAcquiring a first intermediate entity list based on all the first initial entity lists corresponding to the first initial entity lists;
and S107, carrying out duplicate removal processing on the first intermediate entity list to obtain A.
3. The data processing system for determining a co-designated event according to claim 2, wherein B is further acquired in step S200 by:
s201, deleting H from HrAcquiring a corresponding text list to obtain a middle text set H ' = { H ' of the event '1，……，H't，……，H's-1}，H'tThe method is characterized by comprising the steps of referring to a text list corresponding to the t-th event in the middle text set of the event, wherein t =1 … … s-1;
s203, determining any H 'from H'tTaking the corresponding event as a second event;
s205, obtaining
Figure 893678DEST_PATH_IMAGE004
，H'tbRefers to the b-th text of the t-th event, where b =1 … … ft，ftThe number of texts of the t-th event is referred to;
s207, to H'tbCarrying out entity extraction to obtain H'tbA corresponding second initial entity list and a second intermediate entity list are obtained based on all the second initial entity lists;
s209, carrying out duplicate removal processing on the second intermediate entity list to obtain B.
4. The data processing system for determining a co-designated event according to claim 1, further comprising the step of, in the step S300:
s301, a first ontology list A ' = { A ' corresponding to A is obtained '1，……，A'α，……，A'm}，A'αThe number of the alpha first ontologies corresponding to A is defined, alpha =1 … … m, and m is the number of the first ontologies corresponding to A;
s303, obtaining A'αCorresponding entity list
Figure 577994DEST_PATH_IMAGE005
，UαgThe method refers to the g-th first entity in the entity list corresponding to the alpha-th first ontology, and g =1 … …
Figure 530907DEST_PATH_IMAGE007
，
Figure 468907DEST_PATH_IMAGE008
Is of A'αThe number of first entities in the corresponding entity list;
s305 according to UαgObtaining UαgCorresponding first Key entity List U'αg={U1 αg，……，Uβ αg，……，Uz αg}，Uβ αgIs beta to the UαgThe corresponding first key entity, β =1 … … z, meets the following condition:
Figure 717223DEST_PATH_IMAGE010
；
s307, according to UαgAnd U'αgObtaining UαgCorresponding first entity similarity list Tαg={T1 αg，……，Tβ αg，……，TZ αg}，Tβ αgIs referred to as UαgAnd Uβ αgEntity similarity between, wherein Tβ αgThe following conditions are met:
Figure 703765DEST_PATH_IMAGE012
wherein, in the step (A),
Figure 42342DEST_PATH_IMAGE014
is referred to as UαgThe corresponding gamma bit value, NK, in the vectorβ γIs referred to as Uβ αgThe corresponding gamma bit value in the vector, gamma =1 … …
Figure DEST_PATH_IMAGE016
，
Figure DEST_PATH_IMAGE018
Is referred to as UαgVector dimension and U in corresponding vectorβ αgCorresponding vector and UαgThe vector dimensions of the corresponding vectors are consistent;
s309 according to TαgAnd acquiring a first entity cluster.
5. The data processing system for determining co-referenceevents as claimed in claim 1, wherein in the step S400, the acquisition method of the second entity cluster is identical to that of the first entity cluster.
6. The data processing system for determining co-referenceable events according to claim 1, wherein F satisfies the following condition:
F=2k/（p+q）。
7. the data processing system for determining co-referenceable events according to claim 1, wherein F satisfies the following condition:
F=k（p+q）/2pq。
8. the data processing system for determining co-referent events according to claim 6 or 7, further comprising the following steps before the step of S700:
when F =2 k/(p + q), F0=F01Wherein F is01Is a preset first similarity threshold;
when F = k (p + q)/2 pq, F0=F02Wherein F is02Is a preset second similarity threshold.
9. The data processing system for determining coreference events of claim 8, wherein the first similarity threshold value takes a value of 0.8.
10. The data processing system for determining co-designated events of claim 8, wherein the second similarity threshold value is 0.85.
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CN114925692A (en) * 2022-07-21 2022-08-19 中科雨辰科技有限公司 Data processing system for acquiring target event
CN115017269A (en) * 2022-08-05 2022-09-06 中科雨辰科技有限公司 Data processing system for determining similar texts
CN115169319A (en) * 2022-07-21 2022-10-11 中科雨辰科技有限公司 Data processing system for identification symbol
CN117435697A (en) * 2023-12-21 2024-01-23 中科雨辰科技有限公司 Data processing system for acquiring core event

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