CA3170100A1 - Text processing method and device and computer-readable storage medium - Google Patents
Text processing method and device and computer-readable storage mediumInfo
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Abstract
The present invention discloses a text processing method, and corresponding device and computer-readable storage medium, wherein the text processing method comprises: obtaining a session content text; classifying the session content text to obtain a target session content text to which a target object corresponds; and identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object, whereby collected objects and their session content are differentiated according to the session contents, and the session content of the collected object is then identified, judged and marked according to the preconstructed classification model in combination with specific content of the dialogue, so as to quickly and efficiently judge whether the collected object is a suspiciously illegal borrowing object.
Description
TEXT PROCESSING METHOD AND DEVICE AND COMPUTER-READABLE
STORAGE MEDIUM
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the technical field of computer information processing, and more particularly to a text processing method and corresponding device and computer-readable storage medium.
Description of Related Art
STORAGE MEDIUM
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the technical field of computer information processing, and more particularly to a text processing method and corresponding device and computer-readable storage medium.
Description of Related Art
[0002] In online borrowing businesses at present, there often occur circumstances in which outlaws make illegal profits through borrowing platforms from a whole industrial chain including fabricating false information, tampering with equipments, manipulating scripts, and taking advantage of technical loopholes to evade surveillance systems and risk-control engines.
[0003] It has been found after previously introducing collection records of collection systems that abnormally overdue clients would mention such suspiciously latent aggregation circumstances in the collection process as "the loan was manipulated through a middleman"
and "via an agency introduced by a friend", etc., it is therefore currently common to make data exploration to certain degrees on to-be-collected users in the after-loan link to judge whether the to-be-collected users exhibit aggregation features, and the specific means employed is to identify these suspicious patterns during processes of sessions.
and "via an agency introduced by a friend", etc., it is therefore currently common to make data exploration to certain degrees on to-be-collected users in the after-loan link to judge whether the to-be-collected users exhibit aggregation features, and the specific means employed is to identify these suspicious patterns during processes of sessions.
[0004] However, when identifying and screening operations are made on sessions during collection processes, it is usually impossible to carry out scenario restorations for the massive Date Regue/Date Received 2022-08-10 quantity of text data because it is impossible to cover the entire keywords by manual check, so that hitting rates are rendered relatively low, the keywords are few, and it is made impossible to enlarge the exploration range according to corpora.
SUMMARY OF THE INVENTION
SUMMARY OF THE INVENTION
[0005] It is an objective of the present invention to provide a text processing method and corresponding device and computer-readable storage medium enabling precise identification as to whether a collected object is a suspiciously illegal borrowing object according to call records.
[0006] To achieve the above objective, the present invention employs the following technical solutions. According to the first aspect, the present invention provides a text processing method that comprises:
[0007] obtaining a session content text;
[0008] classifying the session content text to obtain a target session content text to which a target object corresponds; and
[0009] identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
[0010] In a preferred mode of execution, the step of obtaining a session content text includes:
[0011] obtaining a session content text generated by conversion on the basis of a call record, wherein the session content text includes a session object number and session sentence fields to which the object number corresponds.
[0012] In a preferred mode of execution, the step of classifying the session content text to obtain a target session content text to which a target object corresponds includes:
[0013] identifying a target session object number on the basis of the session sentence fields and obtaining a target session sentence field to which the target session object number Date Regue/Date Received 2022-08-10 corresponds, wherein the target session content text includes the target session sentence field.
[0014] In a preferred mode of execution, the step of identifying a target session object number on the basis of the session sentence fields and obtaining a target session sentence field to which the target session object number corresponds includes:
[0015] identifying a first preset field in the session sentence fields;
[0016] marking the session object number, to which the session sentence fields that contain the first preset field correspond, as a reference session object number, wherein the session sentence fields to which the reference session object number corresponds constitute a reference session content text; and wherein
[0017] the remaining part in the session sentence fields other than the reference session content text is the target session sentence field, and the session object number to which the target session sentence field corresponds is the target session object number.
[0018] In a preferred mode of execution, the step of identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object includes:
[0019] obtaining a target identification label and a target field to which the target identification label corresponds on the basis of the preconstructed classification model and the target session sentence field;
[0020] judging whether the target identification label is correct on the basis of the reference session content text and the target session content text;
[0021] if yes, marking the target session object number with the target identification label;
[0022] if not, updating the target identification label and marking the target session object number with the updated target identification label.
[0023] In a preferred mode of execution, the step of judging whether the target identification label is correct on the basis of the reference session content text and the target session content text includes:
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
[0024] judging whether a session sentence field adjacent to the target field in the reference session content text contains a second preset field;
[0025] if yes, indicating that the target identification label is correct;
[0026] if not, judging whether the target identification label is correct on the basis of the target session content text.
[0027] In a preferred mode of execution, the step of judging whether the target identification label is correct on the basis of the target session content text includes:
[0028] obtaining probability distribution of each preset identification label on the basis of the classification model and the target session sentence field;
[0029] judging whether standard deviation of the probability distribution is greater than a preset threshold;
[0030] if yes, updating the target identification label with the preset identification label having the maximum probability value;
[0031] if not, calculating a probability value, to which each preset identification label corresponds, on the basis of the target session sentence field and a previously counted probability value of conversion from a current dialogue intent type to a dialogue type of the next round, and choosing the preset identification label, to which the probability value having the maximum value corresponds, to update the target identification label.
[0032] In a preferred mode of execution, prior to the step of identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object, the method further comprises:
[0033] rectifying the target session content text on the basis of a preconstructed rectification database.
[0034] According to the second aspect, the present invention provides a text processing device that comprises:
[0035] an obtaining module, for obtaining a session content text;
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
[0036] a classifying module, for classifying the session content text to obtain a target session content text to which a target object corresponds; and
[0037] an identifying and marking module, for identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
[0038] According to the third aspect, the present invention provides a computer-readable storage medium storing thereon a computer program that performs steps of any text processing method provided by the first aspect when it is executed by a processor.
[0039] Advantages of the present invention are as follows. Provided are a text processing method and corresponding device and computer-readable storage medium, wherein the text processing method comprises: obtaining a session content text; classifying the session content text to obtain a target session content text to which a target object corresponds; and identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object, whereby collected objects and their session content are differentiated according to the session contents, and the session content of the collected object is then identified, judged and marked according to the preconstructed classification model in combination with specific content of the dialogue, so as to quickly and efficiently judge whether the collected object is a suspiciously illegal borrowing object.
BRIEF DESCRIPTION OF THE DRAWINGS
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] To more clearly describe the technical solutions in the embodiments of the present application or the prior-art technology, drawings required to illustrate the embodiments will be briefly introduced below. Apparently, the drawings introduced below are merely directed to some embodiments of the present application, while persons ordinarily skilled in the art may further acquire other drawings on the basis of these drawings without spending creative Date Regue/Date Received 2022-08-10 effort in the process.
[0041] Fig. 1 is a flowchart illustrating the text processing method provided by Embodiment 1 of the present invention;
[0042] Fig. 2 is a view illustrating the content of the session content text in Embodiment 1 of the present invention;
[0043] Fig. 3 is a flowchart illustrating judgement as to whether the target identification label is correct on the basis of the target session content text in the text processing method provided by Embodiment 1 of the present invention;
[0044] Fig. 4 is a view presenting a storage table generated in the text processing method provided by Embodiment 1 of the present invention; and
[0045] Fig. 5 is a view illustrating the structure of the text processing device provided by Embodiment 2 of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
DETAILED DESCRIPTION OF THE INVENTION
[0046] The technical solutions in the embodiments of the present application will be clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present application. Any other embodiments makeable by persons ordinarily skilled in the art on the basis of the embodiments in the present application without creative effort shall all fall within the protection scope of the present application.
[0047] As noted in the Description of Related Art, when an abnormally overdue borrowing user Date Regue/Date Received 2022-08-10 is urged for collection by a collector, key sentences often appear in the dialogue, wherein manual mode is currently employed to screen the key sentences from the collection session record to identify whether the overdue borrowing user is abnormal, but due to restrictions of progress and precision by manual check, it is impossible to quickly and accurately determine the abnormal identification of the overdue borrowing user.
[0048] To address the above problem, the present application introduces the NLP (Natural Language Processing) technology to process collection dialogue contents, constructs a text classification model to extract therefrom target session sentences containing specific labels, identifies user identifications accurately from user patterns, obtains key sentence fields to obtain more evidence to facilitate feedback of circumstances concerning intervention of intermediaries in the after-loan link, further digs for suspicious aggregation of intermediaries out of hit user groups, and makes it easy for subsequent tracking change tendencies of overdue borrowing users of different classifications to facilitate early warning at the same time.
[0049] Embodiment 1: this embodiment provides a text processing method, with reference to Fig. 1, the method comprises the following steps.
[0050] Si - obtaining a session content text.
[0051] In a preferred mode of execution, this step includes:
[0052] obtaining a session content text generated by conversion on the basis of a call record, wherein the session content text includes a session object number and session sentence fields to which the object number corresponds.
[0053] Preferably, the session content text obtained in this embodiment is generated through conversion from a quality-inspected call record.
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
[0054] Since there are usually two dialoging parties in a call record, to facilitate subsequent differentiated processing, besides session content text generated by conversion from speeches, the session content text further includes each session object number, more specifically, before the session content text of each sentence of dialogue, there is the session object number of this sentence of dialogue. Of course, the session content text can further include the duration or a time node identifier of each sentence of dialogue, to which no restriction is made in this embodiment.
[0055] S2 - classifying the session content text to obtain a target session content text to which a target object corresponds.
[0056] In a preferred mode of execution, this step includes:
[0057] identifying a target session object number on the basis of the session sentence fields and obtaining a target session sentence field to which the target session object number corresponds, wherein the target session content text includes the target session sentence field.
[0058] More preferably, this step specifically includes the following.
[0059] S21 - identifying a first preset field in the session sentence fields.
[0060] The first preset field is an identifier field capable of performing identification judgment.
[0061] S22 - marking the session object number, to which the session sentence fields that contain the first preset field correspond, as a reference session object number, wherein the session sentence fields to which the reference session object number corresponds constitute a reference session content text; and
[0062] wherein the remaining part in the session sentence fields other than the reference session content text is the target session sentence field, and the session object number to which the target session sentence field corresponds is the target session object number.
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
[0063] Specifically, when telephone collection is made by a collector, greeting will be made after the telephone has been connected, for example, "Mr. x" or "Madam X", accordingly, the "Mr." and "Madam" are set as first preset fields, by identifying the "Mr."
or "Madam"
field in the session sentence fields, it can be judged that the session object number to which the session sentence fields containing this field correspond is the collector number, namely a reference session object number, and all session sentence fields to which this reference session object number corresponds constitute a reference session content text.
More preferably, by identifying the first preset field in a preset number of the first session sentence fields in the session sentence fields, it is judged that the session object number to which the session sentence fields containing this field correspond is the reference session object number.
The number of the other party in the dialogue is the borrowing user number, namely a target object number, and the session sentence fields to which the target object number corresponds constitute borrower session content, namely a target session content text.
or "Madam"
field in the session sentence fields, it can be judged that the session object number to which the session sentence fields containing this field correspond is the collector number, namely a reference session object number, and all session sentence fields to which this reference session object number corresponds constitute a reference session content text.
More preferably, by identifying the first preset field in a preset number of the first session sentence fields in the session sentence fields, it is judged that the session object number to which the session sentence fields containing this field correspond is the reference session object number.
The number of the other party in the dialogue is the borrowing user number, namely a target object number, and the session sentence fields to which the target object number corresponds constitute borrower session content, namely a target session content text.
[0064] Exemplarily, "1" and "2" represents the collector and the borrowing user, respectively, and the role of the collector is identified through "Mr." or "Madam"
information contained in the first ten sentences of the dialogue. As shown in Fig. 2, number "1"
represents the collector, number "2" represents the borrowing user, by identifying the "Mr."
field it is determined that the session object number "1" to which the session sentence fields containing the "Mr." field correspond is the collector number, namely the reference session object number, and all session sentence fields to which the session object number "1"
corresponds constitute the reference session content text. The remaining session sentence fields other than the reference session content text in the session sentence fields are target session sentence fields, and all target session sentence fields constitute the session content text of the target object, namely the borrowing user, that is to say, all session sentence fields to which session object number "2" corresponds constitute the session content text of the target object.
Sentences corresponding to "1" serve as collector patterns, and sentences corresponding to "2" serve as user patterns.
Date Regue/Date Received 2022-08-10
information contained in the first ten sentences of the dialogue. As shown in Fig. 2, number "1"
represents the collector, number "2" represents the borrowing user, by identifying the "Mr."
field it is determined that the session object number "1" to which the session sentence fields containing the "Mr." field correspond is the collector number, namely the reference session object number, and all session sentence fields to which the session object number "1"
corresponds constitute the reference session content text. The remaining session sentence fields other than the reference session content text in the session sentence fields are target session sentence fields, and all target session sentence fields constitute the session content text of the target object, namely the borrowing user, that is to say, all session sentence fields to which session object number "2" corresponds constitute the session content text of the target object.
Sentences corresponding to "1" serve as collector patterns, and sentences corresponding to "2" serve as user patterns.
Date Regue/Date Received 2022-08-10
[0065] In a preferred mode of execution, after S2 and prior to S3, the method further comprises:
[0066] SA - rectifying the target session content text on the basis of a preconstructed rectification database.
[0067] Specifically, asr (accent speech recognition) is inferior in the recognition effect when faced with regional dialects, session content texts generated by conversion contain erroneous fields, and the introduction of a text rectifying function greatly helps enhance the classification effect. The preconstructed rectification database is a black industry rectification knowledge base constructed on basis of previously collected collection contents and via a finance knowledge base; exemplarily, the preconstructed rectification database is a black industry rectification knowledge base constructed on basis of previously collected one hundred thousand pieces of collection contents and via a finance knowledge base. The black industry rectification knowledge base has types 2-gram, 3-gram, and 4-gram, with formats shown below: the 2-gram corresponds to "Mr.: T!'ft, _____________________ tEIF1 (all Chinese homophones of the Chinese pronunciation of 'MC)", the 3-gram corresponds to "no problem:
(all Chinese homophones of the Chinese pronunciation of 'no problem')", and the 4-gram corresponds to "financial management: itAft fl 914 )3", ft] 914 , fl 914 (all Chinese homophones with antecedent modifiers of the Chinese pronunciation of 'financial management')"; if erroneous terms are matched in a sentence, these terms are replaced with the corresponding correct terms.
(all Chinese homophones of the Chinese pronunciation of 'no problem')", and the 4-gram corresponds to "financial management: itAft fl 914 )3", ft] 914 , fl 914 (all Chinese homophones with antecedent modifiers of the Chinese pronunciation of 'financial management')"; if erroneous terms are matched in a sentence, these terms are replaced with the corresponding correct terms.
[0068] S3 - identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
[0069] In a preferred mode of execution, this step includes the following.
[0070] S31 - obtaining a target identification label and a target field to which the target identification label corresponds on the basis of the preconstructed classification model and Date Regue/Date Received 2022-08-10 the target session sentence field.
[0071] Specifically, the preconstructed classification model's functions are to identify a pattern of the borrowing user, namely the target field, and to assign the target field to the corresponding preset identification label type.
[0072] The preconstructed classification model is obtained through the following method:
[0073] constructing a machine-learning model;
[0074] employing a corpus training set to train the machine-learning model and obtain a preconstructed classification model, wherein the corpus training set includes corpora with previously well-marked identification labels, there are altogether 1090 pieces of corpora, and the previously marked identification labels include whether being the given person, identification acquaintance, identification negation, identification questioning, and others;
[0075] constructing a rule classification model, for identifying a label "commissioned to intermediary" ¨ this is so because the label "commissioned to intermediary"
has very few data, with which it is difficult to train the machine-learning model; the preconstructed classification model includes the aforementioned well-trained machine-learning model and rule classification model. The machine-learning model can be any of a MultinomialNB, LogisticRegression, RandomForestClassifier, SVM, and Fasttext models, and the classification precision rates of the various models are as shown in the following Table. The Fasttext classification model is preferentially selected in this embodiment.
has very few data, with which it is difficult to train the machine-learning model; the preconstructed classification model includes the aforementioned well-trained machine-learning model and rule classification model. The machine-learning model can be any of a MultinomialNB, LogisticRegression, RandomForestClassifier, SVM, and Fasttext models, and the classification precision rates of the various models are as shown in the following Table. The Fasttext classification model is preferentially selected in this embodiment.
[0076]
LogisticRegression MultinomialNB RandomForestClassifier SVM Fasttext 0.7996 0.7990 0.5368 0.8083 0.8152
LogisticRegression MultinomialNB RandomForestClassifier SVM Fasttext 0.7996 0.7990 0.5368 0.8083 0.8152
[0077] After a target session sentence has been input into the preconstructed classification model, the preconstructed classification model outputs a corresponding target identification label and a target field to which the identification label corresponds.
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
[0078] Exemplarily, the five pieces of target session sentences to which the target session object number, namely number "2", as obtained in the foregoing step corresponds are sent into the text classifier piece by piece, sentences corresponding to intents as "whether being the given person", "identification acquaintance", "identification negation", "identification questioning"
and "commissioned to intermediary" are identified, sentences of the type "others" are filtered away, a target identification label of "identification confirmation" is obtained via classification, and target fields corresponding thereto are ["I say to you", "go, of course", "no money, I've told you so", "um, um"'
and "commissioned to intermediary" are identified, sentences of the type "others" are filtered away, a target identification label of "identification confirmation" is obtained via classification, and target fields corresponding thereto are ["I say to you", "go, of course", "no money, I've told you so", "um, um"'
[0079] S32 - judging whether the target identification label is correct on the basis of the reference session content text and the target session content text;
[0080] if yes, entering step S33; if not, entering step S34.
[0081] As can be seen from the target field to which the target identification label corresponds as obtained through the classification model, there are many erroneous data in the classified target fields, because the classification model merely classifies single sentences so it tends easily to generate classification errors, it is required at this time to combine with contextual information and to make use of contextual patterns of the borrowing user and the collector to verify whether the target identification label obtained by the classification model is correct.
[0082] In a preferred mode of execution, this step includes the following.
[0083] S321 - judging whether a session sentence field adjacent to the target field in the reference session content text contains a second preset field.
[0084] If yes, the target identification label is correct; if not, step S322 is entered.
[0085] Specifically, the second preset field is a keyword for identification query, and keywords for identification query include "Mr.", "Madam", "Hi", "How are you", etc., by identifying Date Regue/Date Received 2022-08-10 whether a session sentence field adjacent to the target field in the reference session content text contains a second preset field, namely identifying whether a collector session sentence adjacent to the target field contains such a field as "Mr." or "Madam" or "Hi"
or "How are you", it is judged whether the target identification label is correct. More preferably, it is judged whether the two rounds of session sentence fields both before and after the target field in the reference session content text contain the second preset field, namely to judge whether the collector session sentences in two rounds of sessions both before and after the target field contain such a field as "Mr." or "Madam" or "Hi" or "How are you", if yes, then the target identification label is correct.
or "How are you", it is judged whether the target identification label is correct. More preferably, it is judged whether the two rounds of session sentence fields both before and after the target field in the reference session content text contain the second preset field, namely to judge whether the collector session sentences in two rounds of sessions both before and after the target field contain such a field as "Mr." or "Madam" or "Hi" or "How are you", if yes, then the target identification label is correct.
[0086] Exemplarily, most circumstances of identification confirmation rest in the opening patterns, and target fields to which the label "identification confirmation"
corresponds are ["I
say to you", "go, of course", "no money, I've told you so", "um, um"].
Firstly, the session sentence fields before the target field "I say to you" in the reference session content text contain the second preset field "Mr.", the following session sentence fields contain "Suning", and both the antecedent and the following session sentence fields contain keywords for identification query, namely second preset fields, then the number of the target session object is correspondingly recorded as 2, it is confirmed that the target identification label is correct, and the target field "I say to you" is stored in an identification confirmation list. The confidence value to which the target field "go, of course" is 1, the target identification label is correct, and is also stored in the identification confirmation list. The session sentence fields "Why has this not been processed yet" and "I have been told, when have I been told" before the target field "no money, I've told you so" in the reference session content text do not contain any keyword for identification query, namely any second preset field, then the confidence value to which the target field corresponds is 0, so "no money, I've told you so"
is not stored in the identification confirmation list, by the same token, "um, um" should also be removed.
corresponds are ["I
say to you", "go, of course", "no money, I've told you so", "um, um"].
Firstly, the session sentence fields before the target field "I say to you" in the reference session content text contain the second preset field "Mr.", the following session sentence fields contain "Suning", and both the antecedent and the following session sentence fields contain keywords for identification query, namely second preset fields, then the number of the target session object is correspondingly recorded as 2, it is confirmed that the target identification label is correct, and the target field "I say to you" is stored in an identification confirmation list. The confidence value to which the target field "go, of course" is 1, the target identification label is correct, and is also stored in the identification confirmation list. The session sentence fields "Why has this not been processed yet" and "I have been told, when have I been told" before the target field "no money, I've told you so" in the reference session content text do not contain any keyword for identification query, namely any second preset field, then the confidence value to which the target field corresponds is 0, so "no money, I've told you so"
is not stored in the identification confirmation list, by the same token, "um, um" should also be removed.
[0087] S322 ¨judging whether the target identification label is correct on the basis of the target Date Regue/Date Received 2022-08-10 session content text.
[0088] If no second preset field is identified from the reference session text in step S321, this indicates that the collector session sentence fields contextually do not contain any key information, but sometimes the user pattern actually contains the intent of classification, and it is required at this time to make use of the user contextual pattern to confirm the target identification label.
[0089] Specifically, this step includes the following.
[0090] S3221 - obtaining probability distribution of each preset identification label on the basis of the classification model and the target session sentence field.
[0091] Specifically, after the target session sentence field has entered the classification model, it is possible to output the probability value of each preset type, namely the probability value of each preset identification label.
[0092] S3222 -judging whether standard deviation of the probability distribution is greater than a preset threshold.
[0093] If yes, step S3223 is entered, if not, step 3224 is entered.
[0094] Specifically, if the probability value of the identification label with the maximum probability value as output from the classification model is by far greater than the probabilities of other identification labels, i.e., the standard deviation of the probability value is relatively large, it can then be considered that the identification label with the maximum probability value is reliable. Exemplarily, a standard deviation threshold is set as 0.2. Of the target session sentence field "I am that", probability distributions output from the classification model are 80% for "identification confirmation", 5% for "identification Date Regue/Date Received 2022-08-10 acquaintance", 5% for "identification negation", 5% for "identification questioning", and 5%
for "others", and the standard deviation of the probability is 0.3, then the identification label "identification confirmation" is reliable. If the predicted probability distributions are relatively close to one another, i.e., the standard deviation is smaller than 0.2, this then means that the classification model cannot determine several intent identification labels with close probabilities with respect to the given sentence, and this circumstance is usually due to the fact that the sentence lacks key information for the classification model to make decisive judgment. At this time, foregoing session sentence fields of the user are required to help judge the current intent, and the process enters step S3224.
for "others", and the standard deviation of the probability is 0.3, then the identification label "identification confirmation" is reliable. If the predicted probability distributions are relatively close to one another, i.e., the standard deviation is smaller than 0.2, this then means that the classification model cannot determine several intent identification labels with close probabilities with respect to the given sentence, and this circumstance is usually due to the fact that the sentence lacks key information for the classification model to make decisive judgment. At this time, foregoing session sentence fields of the user are required to help judge the current intent, and the process enters step S3224.
[0095] S3223 ¨ updating the target identification label with the preset identification label having the maximum probability value.
[0096] S3224 - calculating a probability value to which each preset identification label corresponds on the basis of the target session sentence field and a probability value of conversion from a current dialogue intent type to a dialogue type of the next round as previously counted, and choosing the preset identification label to which the probability value having the maximum value corresponds to update the target identification label.
[0097] Specifically, the probability value of conversion from a current dialogue intent type to a dialogue type of the next round as previously counted is obtained through the following method: firstly, dialogue transfer probabilities are counted on the basis of identification label results marked with great quantities of collection dialogues, a dialogue transfer probability is the probability value of converting from the dialogue intent type of the current round to the dialogue type of the next round. For instance, the current user pattern intent type is "identification negation", the transfer probability for the dialogue of the next round to correspond to "identification acquaintance" is 0.6, the probability to correspond to "identification confirmation" is 0.2, the probability to correspond to "identification negation"
is 0.15, and the probability to correspond to "identification questioning" is 0.05; intent Date Regue/Date Received 2022-08-10 transfer probability values are as shown in the following table:
is 0.15, and the probability to correspond to "identification questioning" is 0.05; intent Date Regue/Date Received 2022-08-10 transfer probability values are as shown in the following table:
[0098]
Identification Identification Identification Identification Others Confirmation Negation Acquaintance Questioning Identification 0.2 0.1 0.1 0.15 0.45 Confirmation Identification 0.1 0.3 0.2 0.2 0.2 Negation Identification 0.2 0.2 0.3 0.1 0.2 Acquaintance Identification 0.1 0.25 0.25 0.1 0.3 Questioning Others 0.15 0.1 0.15 0.1 0.5
Identification Identification Identification Identification Others Confirmation Negation Acquaintance Questioning Identification 0.2 0.1 0.1 0.15 0.45 Confirmation Identification 0.1 0.3 0.2 0.2 0.2 Negation Identification 0.2 0.2 0.3 0.1 0.2 Acquaintance Identification 0.1 0.25 0.25 0.1 0.3 Questioning Others 0.15 0.1 0.15 0.1 0.5
[0099] Black industry collection corpora involve many circumstances of multiple rounds of dialogues, effective information contained in the previous round of dialogue might be insufficient, multiple rounds of user dialogue information are added to the classification model here and an identification label to which the target session sentence field of each round of dialogue corresponds is output. Suppose that the probability value for the first i round of identification label to be transferred to the current identification label is pi, the probability of the current identification label is q, a; represents the ith round transfer probability weight, the farther the current sentence is distanced, the smaller will be the cu value, the current identification label probability value pfinal is n f inal = ai * pi *q, the final probability is usually calculated in accordance with the intent values of three rounds of user dialogues, let ai=0.5, a2=0.33, and a3=0.17, the identification label with the highest probability value is found out, and the target identification label is updated with this identification label.
[0100] S33 ¨ marking the target session object number with the identification label.
[0101] Specifically, the type label ["identification confirmation"' is written in the category type field to mark the object session object number. More preferably, the two sentences "I say to you" and "go, of course" as well as their corresponding label Date Regue/Date Received 2022-08-10 "identification confirmation" are written in the category field.
[0102] S34 ¨ updating the identification label and marking the target session object number with the updated target identification label.
[0103] More preferably, the method further comprises:
[0104] S4 ¨ generating a storage table on the basis of the session content text, the target identification label and the target field.
[0105] Preferably, the storage table further includes therein call record IDs and confidence values in step S321, and the storage table is of the hive format. The text processing method provided by this embodiment comprises obtaining a session content text;
classifying the session content text to obtain a target session content text to which a target object corresponds;
and identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object, whereby collected objects and their session content are differentiated according to the session contents, and the session content of the collected object is then identified, judged, and marked according to the preconstructed classification model in combination with specific content of the dialogue, so as to quickly and highly effectively judge whether the collected object is a suspiciously illegal borrowing object.
classifying the session content text to obtain a target session content text to which a target object corresponds;
and identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object, whereby collected objects and their session content are differentiated according to the session contents, and the session content of the collected object is then identified, judged, and marked according to the preconstructed classification model in combination with specific content of the dialogue, so as to quickly and highly effectively judge whether the collected object is a suspiciously illegal borrowing object.
[0106] Embodiment 2: this embodiment provides a text processing device, as shown in Fig. 5, the device comprises:
[0107] an obtaining module 51, for obtaining a session content text;
[0108] a classifying module 52, for classifying the session content text to obtain a target session content text to which a target object corresponds; and
[0109] an identifying and marking module 53, for identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
[0110] In a preferred mode of execution, the obtaining module 51 is employed for obtaining a session content text generated by conversion on the basis of a call record, wherein the session content text includes a session object number and session sentence fields to which the object number corresponds.
[0111] More preferably, the classifying module 52 is employed for:
[0112] identifying a target session object number on the basis of the session sentence fields and obtaining a target session sentence field to which the target session object number corresponds, wherein the target session content text includes the target session sentence field.
[0113] More preferably, the classifying module 52 includes:
[0114] an identifying submodule 521, for identifying a first preset field in the session sentence fields;
[0115] a classifying submodule 522, for marking the session object number, to which the session sentence fields that contain the first preset field correspond, as a reference session object number, wherein the session sentence fields to which the reference session object number corresponds constitute a reference session content text; and
[0116] wherein the remaining part in the session sentence fields other than the reference session content text is the target session sentence field, and the session object number to which the target session sentence field corresponds is the target session object number.
[0117] More preferably, the identifying and marking module 53 includes:
[0118] an obtaining submodule 531, for obtaining a target identification label and a target field to which the target identification label corresponds on the basis of the preconstructed classification model and the target session sentence field;
[0119] a judging submodule 532, for judging whether the target identification label is correct on the basis of the reference session content text and the target session content text;
[0120] an marking submodule 533, for marking the target session object number with the target Date Regue/Date Received 2022-08-10 identification label when the judging submodule 532 judges that the target identification label is correct on the basis of the reference session content text and the target session content text;
and
and
[0121] a label updating submodule 534, for updating the target identification label and marking the target session object number with the updated target identification label when the judging submodule 532 judges that the target identification label is not correct on the basis of the reference session content text and the target session content text.
[0122] More preferably, the judging submodule 533 includes:
[0123] a first judging unit 5331, for judging whether a session sentence field adjacent to the target field in the reference session content text contains a second preset field; and
[0124] a second judging unit 5332, for judging whether the target identification label is correct on the basis of the target session content text.
[0125] More preferably, the second judging unit 5332 includes:
[0126] an obtaining subunit 53321, for obtaining probability distribution of each preset identification label on the basis of the classification model and the target session sentence field;
[0127] a judging subunit 53322, for judging whether standard deviation of the probability distribution is greater than a preset threshold;
[0128] if yes, the label updating submodule 534 updates the target identification label with the preset identification label having the maximum probability value; and
[0129] a calculating and choosing subunit 53323, for calculating a probability value to which each preset identification label corresponds on the basis of the target session sentence field and a probability value of conversion from a current dialogue intent type to a dialogue type of the next round as previously counted, and choosing the preset identification label to which the probability value having the maximum value corresponds, at which time the label updating submodule 534 updates the target identification label with the preset identification label to which the probability value having the maximum value corresponds.
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
[0130] In a preferred mode of execution, the device further comprises:
[0131] a rectifying module 54, for rectifying the target session content text on the basis of a preconstructed rectification database before the identifying and marking module 53 identifies the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
[0132] The text processing device provided by this embodiment is employed for executing the text processing method provided by Embodiment 1, and the advantageous effects achieved thereby are identical with the advantageous effects achievable by the text processing method provided by Embodiment 1, so these are not redundantly described in this context.
[0133] As should be noted, when the text processing device provided by this embodiment executes a text processing method, it is merely exemplarily explained by being divided into the aforementioned various functional modules, whereas it is possible, in actual application, to assign the above functions to different functional modules for completion according to requirements, that is to say, the internal structure of the device is classified into different functional modules to complete the entire or partial functions as described above. In addition, since the text processing device provided by this embodiment pertains to the same conception as the text processing method, see the method embodiment for its specific implementation process, while no repetition is redundantly made in this context.
[0134] Embodiment 3: this embodiment provides a computer-readable storage medium that stores a computer program thereon, and the computer program realizes any of the following steps when it is executed by a processor:
[0135] obtaining a session content text;
[0136] classifying the session content text to obtain a target session content text to which a target object corresponds; and
[0137] identifying the target session content text, on the basis of a preconstructed classification Date Regue/Date Received 2022-08-10 model and the session content text, to mark the target object.
[0138] The computer-readable storage medium provided by this embodiment is employed for processing and executing the steps of the text processing method provided by Embodiment 1, and the advantageous effects achieved thereby are identical with the advantageous effects achievable by the text processing method provided by Embodiment 1, so these are not redundantly described in this context.
[0139] As understandable to persons ordinarily skilled in the art, the entire or partial steps realizing the foregoing embodiments can be completed via hardware, or via a program that instructs relevant hardware, wherein the program can be stored in a computer-readable storage medium, and the storage medium can be, but is not limited to be, a read-only memory, a magnetic disk, or an optical disk.
[0140] Of course, the foregoing embodiments are merely meant to explain technical conception and characteristics of the present invention, and aim to enable technicians familiar with the technology to learn of and implement the contents of the present invention, but the protection scope of the present invention should not be restricted thereby. Any modification made in accordance with the spiritual essence of the main technical solution of the present invention shall be covered by the protection scope of the present invention.
Date Regue/Date Received 2022-08-10
Date Regue/Date Received 2022-08-10
Claims (10)
1. A text processing method, characterized in that the method comprises:
obtaining a session content text;
classifying the session content text to obtain a target session content text to which a target object corresponds; and identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
obtaining a session content text;
classifying the session content text to obtain a target session content text to which a target object corresponds; and identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
2. The text processing method according to Claim 1, characterized in that the step of obtaining a session content text includes:
obtaining a session content text generated by conversion on the basis of a call record, wherein the session content text includes a session object number and session sentence fields to which the object number corresponds.
obtaining a session content text generated by conversion on the basis of a call record, wherein the session content text includes a session object number and session sentence fields to which the object number corresponds.
3. The text processing method according to Claim 2, characterized in that the step of classifying the session content text to obtain a target session content text to which a target object corresponds includes:
identifying a target session object number on the basis of the session sentence fields and obtaining a target session sentence field to which the target session object number corresponds, wherein the target session content text includes the target session sentence field.
identifying a target session object number on the basis of the session sentence fields and obtaining a target session sentence field to which the target session object number corresponds, wherein the target session content text includes the target session sentence field.
4. The text processing method according to Claim 3, characterized in that the step of identifying a target session object number on the basis of the session sentence fields and obtaining a target session sentence field to which the target session object number corresponds includes:
identifying a first preset field in the session sentence fields;
marking the session object number, to which the session sentence fields that contain the first preset field correspond, as a reference session object number, wherein the session sentence fields to which the reference session object number corresponds constitute a reference session content text; and the remaining part in the session sentence fields other than the reference session content text is the target session sentence field, and the session object number to which the target session sentence field corresponds is the target session object number.
identifying a first preset field in the session sentence fields;
marking the session object number, to which the session sentence fields that contain the first preset field correspond, as a reference session object number, wherein the session sentence fields to which the reference session object number corresponds constitute a reference session content text; and the remaining part in the session sentence fields other than the reference session content text is the target session sentence field, and the session object number to which the target session sentence field corresponds is the target session object number.
5. The text processing method according to Claim 4, characterized in that the step of identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object includes:
obtaining a target identification label and a target field to which the target identification label corresponds on the basis of the preconstructed classification model and the target session sentence field;
judging whether the target identification label is correct on the basis of the reference session content text and the target session content text;
if yes, marking the target session object number with the target identification label;
if not, updating the target identification label and marking the target session object number with the updated target identification label.
obtaining a target identification label and a target field to which the target identification label corresponds on the basis of the preconstructed classification model and the target session sentence field;
judging whether the target identification label is correct on the basis of the reference session content text and the target session content text;
if yes, marking the target session object number with the target identification label;
if not, updating the target identification label and marking the target session object number with the updated target identification label.
6. The text processing method according to Claim 5, characterized in that the step of judging whether the target identification label is correct on the basis of the reference session content text and the target session content text includes:
judging whether a session sentence field adjacent to the target field in the reference session content text contains a second preset field;
if yes, indicating that the target identification label is correct;
if not, judging whether the target identification label is correct on the basis of the target session content text.
judging whether a session sentence field adjacent to the target field in the reference session content text contains a second preset field;
if yes, indicating that the target identification label is correct;
if not, judging whether the target identification label is correct on the basis of the target session content text.
7. The text processing method according to Claim 6, characterized in that the step of judging whether the target identification label is correct on the basis of the target session content text includes:
obtaining probability distribution of each preset identification label on the basis of the classification model and the target session sentence field;
judging whether standard deviation of the probability distribution is greater than a preset threshold;
if yes, updating the target identification label with the preset identification label having the maximum probability value;
if not, calculating a probability value, to which each preset identification label corresponds, on the basis of the target session sentence field and a previously counted probability value of conversion from a current dialogue intent type to a dialogue type of the next round, and choosing the preset identification label, to which the probability value having the maximum value corresponds, to update the target identification label.
obtaining probability distribution of each preset identification label on the basis of the classification model and the target session sentence field;
judging whether standard deviation of the probability distribution is greater than a preset threshold;
if yes, updating the target identification label with the preset identification label having the maximum probability value;
if not, calculating a probability value, to which each preset identification label corresponds, on the basis of the target session sentence field and a previously counted probability value of conversion from a current dialogue intent type to a dialogue type of the next round, and choosing the preset identification label, to which the probability value having the maximum value corresponds, to update the target identification label.
8. The text processing method according to Claim 4, characterized in that, prior to the step of identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object, the method further comprises:
rectifying the target session content text on the basis of a preconstructed rectification database.
rectifying the target session content text on the basis of a preconstructed rectification database.
9. A text processing device, characterized in that the device comprises:
an obtaining module, for obtaining a session content text;
a classifying module, for classifying the session content text to obtain a target session content text to which a target object corresponds; and an identifying and marking module, for identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
an obtaining module, for obtaining a session content text;
a classifying module, for classifying the session content text to obtain a target session content text to which a target object corresponds; and an identifying and marking module, for identifying the target session content text, on the basis of a preconstructed classification model and the session content text, to mark the target object.
10. A computer-readable storage medium, storing a computer program thereon, characterized in that steps of the method as recited in anyone of Claims 1 to 8 are realized when the computer program is executed by a processor.
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| CN107343077B (en) * | 2016-04-28 | 2019-12-13 | 腾讯科技（深圳）有限公司 | Method, device and equipment for identifying malicious phone and establishing identification model |
| CN107886955B (en) * | 2016-09-29 | 2021-10-26 | 百度在线网络技术（北京）有限公司 | Identity recognition method, device and equipment of voice conversation sample |
| CN109241256B (en) * | 2018-08-20 | 2022-09-27 | 百度在线网络技术（北京）有限公司 | Dialogue processing method and device, computer equipment and readable storage medium |
| CN109543030B (en) * | 2018-10-12 | 2023-04-07 | 平安科技（深圳）有限公司 | Method, device, equipment and storage medium for classifying session texts of customer service robot |
| CN110136727B (en) * | 2019-04-16 | 2024-04-16 | 平安科技（深圳）有限公司 | Speaker identification method, device and storage medium based on speaking content |
| CN112836025A (en) * | 2019-11-22 | 2021-05-25 | 航天信息股份有限公司 | Intention identification method and device |
| CN111382270A (en) * | 2020-03-05 | 2020-07-07 | 中国平安人寿保险股份有限公司 | Intention recognition method, device and equipment based on text classifier and storage medium |
| CN111695352A (en) * | 2020-05-28 | 2020-09-22 | 平安科技（深圳）有限公司 | Grading method and device based on semantic analysis, terminal equipment and storage medium |
| CN111508501B (en) * | 2020-07-02 | 2020-09-29 | 成都晓多科技有限公司 | Voice recognition method and system with accent for telephone robot |
| CN112100349B (en) * | 2020-09-03 | 2024-03-19 | 深圳数联天下智能科技有限公司 | Multi-round dialogue method and device, electronic equipment and storage medium |
| CN112307168B (en) * | 2020-10-30 | 2023-11-07 | 康键信息技术（深圳）有限公司 | Artificial intelligence-based inquiry session processing method and device and computer equipment |
| CN113066499B (en) * | 2021-03-12 | 2022-06-03 | 四川大学 | Method and device for identifying identity of land-air conversation speaker |
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