CN111178075A - Online customer service log analysis method, device and equipment - Google Patents
Online customer service log analysis method, device and equipment Download PDFInfo
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- CN111178075A CN111178075A CN201911313840.2A CN201911313840A CN111178075A CN 111178075 A CN111178075 A CN 111178075A CN 201911313840 A CN201911313840 A CN 201911313840A CN 111178075 A CN111178075 A CN 111178075A
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
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- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
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Abstract
The invention discloses an online customer service log analysis method, an online customer service log analysis device and online customer service log analysis equipment. Wherein the method comprises the following steps: the method comprises the steps of training a named entity recognition model of an online customer service log, extracting log entity information of sentences in the online customer service log according to the trained named entity recognition model, and extracting entity category association of the sentences in the online customer service log according to the extracted log entity information and through co-occurrence relations among entity category keywords. By the method, the association among the keywords of the online customer service log which is the final output result can be expressed, and the association among different keywords of the online customer service log can be analyzed.
Description
Technical Field
The invention relates to the technical field of customer service logs, in particular to an online customer service log analysis method, an online customer service log analysis device and online customer service log analysis equipment.
Background
The existing online customer service log analysis scheme generally adopts a word frequency statistics, a keyword extraction algorithm and a topic model LDA (Latent Dirichlet Allocation) mode to perform statistics on main keywords in the online customer service log.
However, in the existing online customer service log analysis scheme, the keywords of the online customer service log are mainly used as the final output result, and the correlation among the keywords cannot be expressed, so that the correlation among different keywords of the online customer service log cannot be analyzed.
Disclosure of Invention
In view of the above, the present invention provides an online customer service log analysis method, an online customer service log analysis device, and an online customer service log analysis device, which can express associations between keywords of an online customer service log as a final output result, and further can analyze associations between different keywords of the online customer service log.
According to one aspect of the invention, an online customer service log analysis method is provided, which comprises the following steps: carrying out named entity recognition model training on the online customer service log; according to the trained named entity recognition model, performing log entity information extraction on sentences in the online customer service log; and according to the extracted log entity information, carrying out entity category correlation extraction on sentences in the online customer service log through the co-occurrence relation among entity category keywords.
The named entity recognition model training of the corpus of the online customer service log comprises the following steps: and (3) carrying out named entity recognition model training on the corpus of the online customer service log by adopting a neural network mode of a bidirectional long-time memory network conditional random field.
Wherein, the extracting of the log entity information of the sentences in the online customer service log according to the trained named entity recognition model comprises: and according to the trained named entity recognition model, extracting the entity information of the log of the sentences in the online customer service log by adopting a keyword extraction mode of each entity category.
Wherein, according to the extracted log entity information, the entity category correlation extraction is carried out on the sentences in the online customer service log through the co-occurrence relationship among the entity category keywords, and the method comprises the following steps: and according to the extracted log entity information, counting the co-occurrence frequency of each entity category keyword, according to the counted co-occurrence frequency, counting the co-occurrence relation among the entity category keywords, and according to the counted co-occurrence relation among the entity category keywords, performing entity category correlation extraction on the sentences in the online customer service log.
After the entity category association extraction is performed on the sentences in the online customer service log through the co-occurrence relationship among the entity category keywords according to the extracted log entity information, the method further comprises the following steps: and carrying out entity category classification on sentences in the online customer service logs after the entity category association extraction.
According to another aspect of the present invention, there is provided an online customer service log analysis apparatus, including: the system comprises a training module, an extraction module and an extraction module; the training module is used for carrying out named entity recognition model training on the online customer service log; the extraction module is used for extracting the log entity information of the sentences in the online customer service log according to the trained named entity recognition model; and the extraction module is used for carrying out entity category correlation extraction on sentences in the online customer service log through the co-occurrence relation among all entity category keywords according to the extracted log entity information.
Wherein, the training module is specifically configured to: and (3) carrying out named entity recognition model training on the corpus of the online customer service log by adopting a neural network mode of a bidirectional long-time memory network conditional random field.
Wherein, the extraction module is specifically configured to: and according to the trained named entity recognition model, extracting the entity information of the log of the sentences in the online customer service log by adopting a keyword extraction mode of each entity category.
Wherein, the extraction module is specifically configured to: and according to the extracted log entity information, counting the co-occurrence frequency of each entity category keyword, according to the counted co-occurrence frequency, counting the co-occurrence relation among the entity category keywords, and according to the counted co-occurrence relation among the entity category keywords, performing entity category correlation extraction on the sentences in the online customer service log.
Wherein, the online customer service log analysis device further comprises: a classification module; and the classification module is used for carrying out entity class classification on the sentences in the online customer service logs after the entity class association extraction.
According to still another aspect of the present invention, there is provided an online customer service log analyzing apparatus including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the above described online customer service log analysis methods.
According to yet another aspect of the present invention, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements the online customer service log analysis method of any one of the above.
It can be found that, according to the above scheme, the named entity recognition model training can be performed on the online customer service log, the log entity information extraction can be performed on the sentences in the online customer service log according to the trained named entity recognition model, and the entity category correlation extraction can be performed on the sentences in the online customer service log according to the extracted log entity information and through the co-occurrence relationship among the entity category keywords, so that the correlation among the keywords of the online customer service log as the final output result can be expressed, and further, the correlation among different keywords of the online customer service log can be analyzed.
Furthermore, the scheme can adopt a neural network mode of a two-way long-and-short-time memory network conditional random field to train the named entity recognition model for the corpus of the online customer service log, and has the advantage that the corresponding characteristic array and the optimal sequence label can be predicted for the corpus and the text information of the online customer service log.
Further, according to the scheme, the sentence in the online customer service log can be subjected to log entity information extraction by adopting an entity category keyword extraction mode according to the trained named entity recognition model, so that the advantage that the sentence in the online customer service log can be subjected to the extraction of the entity category keywords is realized, and the correlation among the keywords of the online customer service log as a final output result can be conveniently expressed.
Furthermore, the above scheme can count the co-occurrence frequency of the keywords of each entity category according to the extracted log entity information, count the co-occurrence relationship among the keywords of each entity category according to the counted co-occurrence frequency, and extract the entity category association of the sentences in the online customer service log according to the counted co-occurrence relationship among the keywords of each entity category, so that the association among the keywords of the online customer service log as a final output result can be expressed, and the association among different keywords of the online customer service log can be analyzed.
Further, the scheme can classify the entity categories of the sentences in the online customer service log after the entity category association extraction, and has the advantages that the frequency of the entity category classification can be used for carrying out frequency sequencing on the customer service items appearing in the online customer service log, so that the more targeted online customer service can be provided conveniently.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of an online customer service log analysis method according to the present invention;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of an online customer service log analysis method of the present invention;
FIG. 3 is a schematic structural diagram of an online customer service log analysis device according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an online customer service log analysis device according to another embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an online customer service log analysis device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be noted that the following examples are only illustrative of the present invention, and do not limit the scope of the present invention. Similarly, the following examples are only some but not all examples of the present invention, and all other examples obtained by those skilled in the art without any inventive work are within the scope of the present invention.
The invention provides an online customer service log analysis method, which can realize the expression of the correlation among the keywords of the online customer service log which is the final output result, and further can analyze the correlation among different keywords of the online customer service log.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of an online customer service log analysis method according to the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same. As shown in fig. 1, the method comprises the steps of:
s101: NER (Named Entity Recognition) model training is carried out on the online customer service log.
The training of the named entity recognition model for the corpus of the online customer service log may include:
a neural network mode of 'BILSTM + CRF' (Bi-directional Long-Short Term Memory + conditional random Field) is adopted to carry out named entity recognition model training on the corpus of the online customer service log, and the advantage is that corresponding feature arrays and optimal sequence labels can be predicted from the corpus context information of the online customer service log.
In this embodiment, the named entity recognition is a very basic task in NLP (Neuro-linear Programming), that is, a named term is recognized from a corpus text of an online customer service log to make a cushion for tasks such as relationship extraction. In a narrow sense, three types of named entities, such as a person name, a place name and an organization name, are recognized, and entity types with obvious composition rules, such as time, currency names and the like, can be recognized in a regular mode and the like. Of course, in a particular domain, various entity types within the domain will be defined accordingly.
S102: and according to the trained named entity recognition model, performing log entity information extraction on sentences in the online customer service log.
Wherein, the extracting of the log entity information of the sentences in the online customer service log according to the trained named entity recognition model may include:
according to the trained named entity recognition model, the sentence in the online customer service log is extracted by adopting the extraction mode of each entity category keyword, so that the advantage that each entity category keyword can be extracted from the sentence in the online customer service log is realized, and the association between the keywords of the online customer service log as a final output result can be conveniently expressed.
In this embodiment, according to the trained named entity recognition model, log entity information is extracted from the sentences in the online customer service log, for example, entity keywords such as "item", "part", "symptom" and the like in the sentences in the online customer service log may be extracted, and a keyword list of each entity category may be obtained for each sentence in the online customer service log.
S103: and according to the extracted log entity information, carrying out entity category correlation extraction on sentences in the online customer service log through the co-occurrence relation among entity category keywords.
Wherein, the extracting entity category association of the sentences in the online customer service log according to the extracted log entity information and the co-occurrence relationship among the entity category keywords may include:
according to the extracted log entity information, the co-occurrence frequency of each entity category keyword is counted, the co-occurrence relation among the entity category keywords is counted according to the counted co-occurrence frequency, and the sentence in the online customer service log is subjected to entity category correlation extraction through the counted co-occurrence relation among the entity category keywords.
After the entity category association extraction is performed on the sentences in the online customer service log through the co-occurrence relationship among the entity category keywords according to the extracted log entity information, the method may further include:
the sentences in the online customer service log after the entity class association and extraction are subjected to entity class classification, so that the method has the advantages that the frequency of the entity class classification can be used for carrying out frequency sequencing on the customer service items appearing in the online customer service log, and the more targeted online customer service can be provided conveniently.
In this embodiment, the association in the entity can be inferred through the co-occurrence relationship between the entity keywords in the same sentence in the online customer service log, for example, the co-occurrence frequency of a certain material keyword and a certain part in the online customer service log can be counted as the statistics of the association relationship between the material and the part. For example, a sentence "i want hyaluronic acid to nose" in the online customer service log, the entity extraction result may be "material", "hyaluronic acid", "part" and "nose", at this time, the keyword co-occurrence of hyaluronic acid and nose is recorded once, the co-occurrence frequency of the keyword is counted, the keyword co-occurrence of high frequency is selected, and the frequently-done item "hyaluronic acid nose hump" can be automatically inferred according to the log.
It can be found that, in this embodiment, the named entity recognition model training may be performed on the online customer service log, the log entity information extraction may be performed on the sentences in the online customer service log according to the trained named entity recognition model, and the entity category association extraction may be performed on the sentences in the online customer service log according to the extracted log entity information and through the co-occurrence relationship between the entity category keywords, so that the association between the keywords of the online customer service log as the final output result can be expressed, and further, the association between different keywords of the online customer service log can be analyzed.
Further, in this embodiment, a neural network mode of a two-way long-and-short-term memory network conditional random field may be adopted to perform named entity recognition model training on the corpus of the online customer service log, which has the advantage of being able to predict the corresponding feature arrays and optimal sequence labels for the corpus and the text information of the online customer service log.
Further, in this embodiment, according to the trained named entity recognition model, the extraction manner of the entity category keywords can be adopted to extract the log entity information of the sentences in the online customer service log, which has the advantages of being capable of extracting the entity category keywords of the sentences in the online customer service log, and being convenient for expressing the association between the keywords of the online customer service log as the final output result.
Further, in this embodiment, the co-occurrence frequency of the keywords of each entity category may be counted according to the extracted log entity information, the co-occurrence relationship between the keywords of each entity category may be counted according to the counted co-occurrence frequency, and the sentences in the online customer service log may be subjected to entity category association extraction according to the counted co-occurrence relationship between the keywords of each entity category, which is advantageous in that the association between the keywords of the online customer service log as the final output result may be expressed, and the association between different keywords of the online customer service log may be analyzed.
Referring to fig. 2, fig. 2 is a schematic flow chart of another embodiment of the online customer service log analysis method according to the present invention. In this embodiment, the method includes the steps of:
s201: and carrying out named entity recognition model training on the online customer service log.
As described above in S101, further description is omitted here.
S202: and according to the trained named entity recognition model, performing log entity information extraction on sentences in the online customer service log.
As described above in S102, further description is omitted here.
S203: and according to the extracted log entity information, carrying out entity category correlation extraction on sentences in the online customer service log through the co-occurrence relation among entity category keywords.
As described above in S103, which is not described herein.
S204: and carrying out entity category classification on sentences in the online customer service logs after the entity category association extraction.
It can be found that, in this embodiment, the sentences in the online customer service log after being extracted by entity category association can be subjected to entity category classification, which has the advantage that the frequency of occurrence of the entity category classification can be used to perform frequency sequencing on the customer service items occurring in the online customer service log, so as to provide more targeted online customer service.
The invention also provides an online customer service log analysis device, which can express the correlation among the keywords of the online customer service log as a final output result, and further can analyze the correlation among different keywords of the online customer service log.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an online customer service log analysis device according to an embodiment of the present invention. In this embodiment, the online customer service log analysis device 30 includes a training module 31, an extraction module 32, and an extraction module 33.
The training module 31 is configured to perform named entity recognition model training on the online customer service log.
The extraction module 32 is configured to extract log entity information of the sentences in the online customer service log according to the trained named entity recognition model.
The extraction module 33 is configured to perform entity category association extraction on the sentences in the online customer service log according to the extracted log entity information and through a co-occurrence relationship between the entity category keywords.
Optionally, the training module 31 may be specifically configured to:
and (3) carrying out named entity recognition model training on the corpus of the online customer service log by adopting a neural network mode of a bidirectional long-time memory network conditional random field.
Optionally, the extraction module 32 may be specifically configured to:
and according to the trained named entity recognition model, extracting the entity information of the log of the sentences in the online customer service log by adopting a keyword extraction mode of each entity category.
Optionally, the extracting module 33 may be specifically configured to:
and according to the extracted log entity information, counting the co-occurrence frequency of the entity category keywords, according to the counted co-occurrence frequency, counting the co-occurrence relation among the entity category keywords, and according to the counted co-occurrence relation among the entity category keywords, performing entity category correlation extraction on the sentences in the online customer service log.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an online customer service log analysis device according to another embodiment of the present invention. Different from the previous embodiment, the online customer service log analysis device 40 of the present embodiment further includes a classification module 41.
The classification module 41 is configured to classify the entity category of the sentences in the online customer service log extracted by the entity category association.
Each unit module of the online customer service log analysis device 30/40 can respectively execute the corresponding steps in the above method embodiments, and therefore, the detailed description of each unit module is omitted here, and please refer to the description of the corresponding steps above.
The present invention further provides an online customer service log analysis device, as shown in fig. 5, including: at least one processor 51; and a memory 52 communicatively coupled to the at least one processor 51; the memory 52 stores instructions executable by the at least one processor 51, and the instructions are executed by the at least one processor 51 to enable the at least one processor 51 to perform the online service log analysis method.
Wherein the memory 52 and the processor 51 are coupled in a bus, which may comprise any number of interconnected buses and bridges, which couple one or more of the various circuits of the processor 51 and the memory 52 together. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor 51 is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor 51.
The processor 51 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory 52 may be used to store data used by the processor 51 in performing operations.
The present invention further provides a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
It can be found that, according to the above scheme, the named entity recognition model training can be performed on the online customer service log, the log entity information extraction can be performed on the sentences in the online customer service log according to the trained named entity recognition model, and the entity category correlation extraction can be performed on the sentences in the online customer service log according to the extracted log entity information and through the co-occurrence relationship among the entity category keywords, so that the correlation among the keywords of the online customer service log as the final output result can be expressed, and further, the correlation among different keywords of the online customer service log can be analyzed.
Furthermore, the scheme can adopt a neural network mode of a two-way long-and-short-time memory network conditional random field to train the named entity recognition model for the corpus of the online customer service log, and has the advantage that the corresponding characteristic array and the optimal sequence label can be predicted for the corpus and the text information of the online customer service log.
Further, according to the scheme, the sentence in the online customer service log can be subjected to log entity information extraction by adopting an entity category keyword extraction mode according to the trained named entity recognition model, so that the advantage that the sentence in the online customer service log can be subjected to the extraction of the entity category keywords is realized, and the correlation among the keywords of the online customer service log as a final output result can be conveniently expressed.
Furthermore, the above scheme can count the co-occurrence frequency of the keywords of each entity category according to the extracted log entity information, count the co-occurrence relationship among the keywords of each entity category according to the counted co-occurrence frequency, and extract the entity category association of the sentences in the online customer service log according to the counted co-occurrence relationship among the keywords of each entity category, so that the association among the keywords of the online customer service log as a final output result can be expressed, and the association among different keywords of the online customer service log can be analyzed.
Further, the scheme can classify the entity categories of the sentences in the online customer service log after the entity category association extraction, and has the advantages that the frequency of the entity category classification can be used for carrying out frequency sequencing on the customer service items appearing in the online customer service log, so that the more targeted online customer service can be provided conveniently.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be substantially or partially implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a part of the embodiments of the present invention, and not intended to limit the scope of the present invention, and all equivalent devices or equivalent processes performed by the present invention through the contents of the specification and the drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An online customer service log analysis method is characterized by comprising the following steps:
carrying out named entity recognition model training on the online customer service log;
according to the trained named entity recognition model, performing log entity information extraction on sentences in the online customer service log;
and according to the extracted log entity information, carrying out entity category correlation extraction on sentences in the online customer service log through the co-occurrence relation among entity category keywords.
2. The method of claim 1, wherein the training of the named entity recognition model for the corpus of the online customer service log comprises:
and (3) carrying out named entity recognition model training on the corpus of the online customer service log by adopting a neural network mode of a bidirectional long-time memory network conditional random field.
3. The method of claim 1, wherein the extracting of log entity information from the sentences in the online customer service log according to the trained named entity recognition model comprises:
and according to the trained named entity recognition model, extracting the entity information of the log of the sentences in the online customer service log by adopting a keyword extraction mode of each entity category.
4. The method as claimed in claim 1, wherein said extracting entity category association of sentences in said online service log according to said extracted entity information of log by co-occurrence relationship among entity category keywords comprises:
and according to the extracted log entity information, counting the co-occurrence frequency of each entity category keyword, according to the counted co-occurrence frequency, counting the co-occurrence relation among the entity category keywords, and according to the counted co-occurrence relation among the entity category keywords, performing entity category correlation extraction on the sentences in the online customer service log.
5. The method as claimed in claim 1, wherein after said extracting entity category association of sentences in said online service log according to said extracted entity information of log by co-occurrence relationship among entity category keywords, further comprising:
and carrying out entity category classification on sentences in the online customer service logs after the entity category association extraction.
6. An online customer service log analysis device, comprising:
the system comprises a training module, an extraction module and an extraction module;
the training module is used for carrying out named entity recognition model training on the online customer service log;
the extraction module is used for extracting the log entity information of the sentences in the online customer service log according to the trained named entity recognition model;
and the extraction module is used for carrying out entity category correlation extraction on sentences in the online customer service log through the co-occurrence relation among all entity category keywords according to the extracted log entity information.
7. The online customer service log analysis device of claim 6, wherein the training module is specifically configured to:
and (3) carrying out named entity recognition model training on the corpus of the online customer service log by adopting a neural network mode of a bidirectional long-time memory network conditional random field.
8. The online customer service log analysis device of claim 6, wherein the extraction module is specifically configured to:
and according to the trained named entity recognition model, extracting the entity information of the log of the sentences in the online customer service log by adopting a keyword extraction mode of each entity category.
9. The online customer service log analysis device of claim 6, wherein the extraction module is specifically configured to:
and according to the extracted log entity information, counting the co-occurrence frequency of each entity category keyword, according to the counted co-occurrence frequency, counting the co-occurrence relation among the entity category keywords, and according to the counted co-occurrence relation among the entity category keywords, performing entity category correlation extraction on the sentences in the online customer service log.
10. The online customer service log analysis device of claim 6, wherein the online customer service log analysis device further comprises:
a classification module;
and the classification module is used for carrying out entity class classification on the sentences in the online customer service logs after the entity class association extraction.
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| CN113315792A (en) * | 2021-07-30 | 2021-08-27 | 深圳市永达电子信息股份有限公司 | Object extraction method and device of network data, electronic equipment and storage medium |
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