CA3176868A1 - Intent identifying method and device for application to intelligent customer service robot - Google Patents
Intent identifying method and device for application to intelligent customer service robotInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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Abstract
An intention recognition method and device for an intelligent customer service robot, relating to the technical field of artificial intelligence. The method comprises: S0, obtaining conversation text of a user; S2, determining whether the conversation text comprises an intention, if yes, executing step S4, if not, ending processing, and if it unable to determine whether the conversation text comprises an intention, executing step S3; S3, performing context expansion on the conversation text, and after step S3, executing step S4; S4, recognizing a named entity set in the conversation text, and determining intention knowledge points associated with the named entity set; S5, representing the conversation text by using a distributed word vector, and performing prediction by using a plurality of pre-trained semantic classification models to obtain a plurality of pieces of semantic information; and S6, combining and optimizing the intention knowledge points and the plurality of pieces of semantic information by using an Ensemble framework to obtain the intention of the user. According to the method and device, an intelligent customer service robot can quickly and accurately recognize the intention of a user, thereby providing guarantee for the robot to accurately answer the user's question.
Description
INTENT IDENTIFYING METHOD AND DEVICE FOR APPLICATION TO
INTELLIGENT CUSTOMER SERVICE ROBOT
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of artificial intelligence technology, and more particularly to an intent identifying method for application to an intelligent customer service robot, and a corresponding device.
Description of Related Art
INTELLIGENT CUSTOMER SERVICE ROBOT
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of artificial intelligence technology, and more particularly to an intent identifying method for application to an intelligent customer service robot, and a corresponding device.
Description of Related Art
[0002] With the rapid development of businesses, artificial intelligence technology has been progressing by leaps and bounds, and the advent of customer service robots effectively shares the workload of human customer service, economizes on personnel cost of enterprises, breaks restrictions in time, manpower, and regionalization, supplies uninterrupted consultation services for 24 hours a day and seven days a week, and alleviates pain points of human customer service. Customer service robots can accept various questions raised by users, and one of the keys bestowing highly effective availability to a customer service robot is whether it can judge out the true intent of a user according to information provided by the user.
[0003] An extremely rapid development of customer service robots has been seen over the recent years notwithstanding, since user-interactive data is involved therein, higher sensitivity is present, and there are very few texts with intents in all the dialogue texts, so the traditional intent identification work is faced with many such challenges as the chat texts are semantically understood not much deeply, and it is impossible to quickly and accurately comprehend the user intent in a shorter dialogue text with the user.
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0004] Accordingly, it is a problem to be urgently dealt with as how to ensure that the intelligent customer service robot quickly and accurately comprehends the user intent, so as to quickly and precisely answer questions raised by the user.
SUMMARY OF THE INVENTION
SUMMARY OF THE INVENTION
[0005] In view of the above, embodiments of the present invention provide an intent identifying method for application to an intelligent customer service robot, and a corresponding device, to realize quick and accurate recognition of user intent by the intelligent customer service robot, and to supply guarantee for the robot to accurately answer questions raised by the user.
[0006] Technical solutions provided by the embodiments of the present invention are as follows.
[0007] According to the first aspect, there is provided an intent identifying method for application to an intelligent customer service robot, and the method comprises the following steps:
[0008] SO ¨ obtaining a dialogue text of a user;
[0009] S2 ¨ judging whether the dialogue text contains any intent, if yes, executing step S4, if not, terminating the process, if impossible to judge, executing step S3;
[0010] S3 ¨ contextually expanding the dialogue text, and executing step S4 after step S3;
[0011] S4 ¨ identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set;
[0012] S5 ¨ expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information; and
[0013] S6 ¨ employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0014] Moreover, prior to step S2, the method further comprises the following step:
[0015] Si ¨ performing text rectification on the dialogue text.
[0016] Further, step Si specifically includes:
[0017] term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text;
[0018] obtaining a rectifying term to which the erroneous segmented term corresponds; and
[0019] replacing the erroneous segmented term in the dialogue text with the rectifying term.
[0020] Further, step S3 specifically includes:
[0021] storing user conversation information with one session as a unit;
[0022] associating with contextual information of the dialogue text, and judging whether the user intent is changed, wherein the contextual information includes an intent identifying result of context of the dialogue text; and
[0023] employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
[0024] Further, step S4 specifically includes:
[0025] performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms;
[0026] matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set; and
[0027] determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
[0028] Further, step S5 specifically includes:
[0029] performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms;
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0030] calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution; and
[0031] inputting the word vectors of the plural segmented terms expressed in distribution to the plural semantically classifying models to output the plural pieces of semantic information.
[0032] Further, step S6 specifically includes:
[0033] determining a final user intent through the Ensemble framework according to the intent knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
[0034] According to the second aspect, there is provided an intent identifying device for application to an intelligent customer service robot, and the device comprises:
[0035] a text obtaining module, for obtaining a dialogue text of a user;
[0036] an intent judging module, for judging whether the dialogue text contains any intent, if yes, executing a process of an entity matching module, if not, terminating the process, if impossible to judge, executing a process of a text expanding module;
[0037] the text expanding module, for contextually expanding the dialogue text, and executing the process of the entity matching module with respect to the expanded dialogue text;
[0038] the entity matching module, for identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set;
[0039] a semantically predicting module, for expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information; and
[0040] a merging and tuning module, for employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
[0041] Moreover, the device further comprises:
[0042] a text rectifying module, for performing text rectification on the dialogue text.
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0043] Further, the text rectifying module is specifically employed for:
[0044] term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text;
[0045] obtaining a rectifying term to which the erroneous segmented term corresponds; and
[0046] replacing the erroneous segmented term in the dialogue text with the rectifying term.
[0047] Further, the text expanding module is specifically employed for:
[0048] storing user conversation information with one session as a unit;
[0049] associating with contextual information of the dialogue text, and judging whether the user intent is changed, wherein the contextual information includes an intent identifying result of context of the dialogue text; and
[0050] employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
[0051] Further, the entity matching module is specifically employed for:
[0052] performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms;
[0053] matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set; and
[0054] determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
[0055] Further, the semantically predicting module is specifically employed for:
[0056] performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms;
[0057] calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution; and
[0058] inputting the word vectors of the plural segmented terms expressed in distribution to the Date Regue/Date Received 2022-09-23 plural semantically classifying models to output the plural pieces of semantic information.
[0059] Further, the merging and tuning module is specifically employed for:
[0060] determining a final user intent through the Ensemble framework according to the intent knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
[0061] In comparison with prior-art technology, the present invention achieves the following advantageous effects:
[0062] 1. When it is impossible to judge whether the dialogue text contains any intent, intent information can be complemented in the user dialogue text by contextually expanding the dialogue text.
[0063] 2. Semantic association among terms is taken into full consideration while features are being extracted by expressing the dialogue text in distributed word vectors and by deep feature mining through a deep learning model.
[0064] 3. An Ensemble framework is employed to merge and tune the entity matching result and the semantically predicting result to obtain the user intent, whereby are achieved to more quickly and accurately identify the user intent, to enhance precision rate in identification of the user intent, and to reduce error and incompleteness in identification of the user intent, so that guarantee is supplied for the customer service robot to correctly answer questions raised by users.
BRIEF DESCRIPTION OF THE DRAWINGS
BRIEF DESCRIPTION OF THE DRAWINGS
[0065] To describe the technical solutions in the embodiments of the present invention more clearly, drawings required for use in the description of the embodiments will be briefly introduced below. Apparently, the drawings introduced below are merely directed to some embodiments of the present invention, and it is possible for persons ordinarily skilled in the art to base on these drawings to acquire other drawings without creative effort being Date Regue/Date Received 2022-09-23 spent in the process.
[0066] Fig. 1 is a flowchart illustrating an intent identifying method for application to an intelligent customer service robot;
[0067] Fig. 2 is a flowchart illustrating specific implementation of step Si in Fig. 1;
[0068] Fig. 3 is a flowchart illustrating specific implementation of step S3 in Fig. 1;
[0069] Fig. 4 is a flowchart illustrating specific implementation of step S4 in Fig. 1;
[0070] Fig. 5 is a flowchart illustrating specific implementation of step S5 in Fig. 1; and
[0071] Fig. 6 is a block diagram illustrating an intent identifying device for application to an intelligent customer service robot.
DETAILED DESCRIPTION OF THE INVENTION
DETAILED DESCRIPTION OF THE INVENTION
[0072] To make more lucid and clear the objectives, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and comprehensively described below in conjunction with accompanying drawings in the embodiments of the present invention. Apparently, the embodiments as described below are merely partial, rather than the entire, embodiments of the present invention. All other embodiments makeable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without spending any creative effort in the process shall all fall within the protection scope of the present invention.
[0073] An embodiment of the present invention provides an intent identifying method for application to an intelligent customer service robot, the method obtains a user intent by Date Regue/Date Received 2022-09-23 contextually expanding a dialogue text in combination with entity matching identification and semantic information prediction, whereby are made possible to more quickly and accurately identify the user intent, to enhance precision rate in identification of the user intent, and to reduce error and incompleteness in identification of the user intent, so that guarantee is supplied for the customer service robot to correctly answer questions raised by users.
[0074] Understandably, the method provided by the embodiment of the present invention is applicable to any intelligent terminal that includes, but is not limited to, a table computer, a personal computer, a smart mobile phone, and a panel computer, etc.
[0075] As should be additionally noted, the terms "first" and "second" etc. as used in the description of the present invention are merely meant for descriptive purposes, rather than for indicating or implying relative importance. In addition, unless explained otherwise in the description of the present invention, the wordings of "plural" and "a plurality of' denote the meaning of "two or more".
[0076] Embodiment 1
[0077] This embodiment of the present invention provides an intent identifying method for application to an intelligent customer service robot, with reference to what is shown in Fig. 1, the method comprises the following steps.
[0078] SO ¨ obtaining a dialogue text of a user.
[0079] The user dialogue can be a speech or a text, when the dialogue is a speech, the user dialogue can be converted from speech to text before execution of the embodiment of the present invention. Besides, the dialogue text can be a long text, and can also be a short text, to which no specific definition is made in the embodiments of the present invention.
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0080] Si ¨ performing text rectification on the dialogue text.
[0081] With reference to what is shown in Fig. 2, the specific implementation process of step Si can include:
[0082] Sll ¨ term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text; and
[0083] 512 ¨ obtaining a rectifying term to which the erroneous segmented term corresponds, and replacing the erroneous segmented term in the dialogue text with the rectifying term.
[0084] Specifically, the rectifying term to which the erroneous segmented term corresponds can be obtained on the basis of a dictionary of wrong words, specifically speaking, with respect to the erroneous segmented term, a rectification confidence degree to which each term in a self-defined standard lexicon corresponds is calculated, and any term whose rectification confidence degree is greater than a preset threshold is taken to serve as the rectifying term. In addition, it is also possible to employ such modes as edit distance or language model to obtain the rectifying term to which the erroneous segmented term corresponds, while the specific obtaining process is not specifically defined in this embodiment.
[0085] In the above step 512, the rectifying term is mainly used for rectifying any erroneous segmented term in the identified text. For example, if an erroneous segmented term in the identified text "l-fEelti WM (big treeta application case)" is "A-IE (big treeta)", the corresponding rectifying term will be "ttlg (big data)".
[0086] As should be noted, step Si is an optional process.
[0087] In this embodiment, by performing text rectification on the dialogue text, the dialogue text with any phrasal error is converted into correct expression that conforms to field Date Regue/Date Received 2022-09-23 logics, so that the user intent can be more accurately identified.
[0088] S2 ¨ judging whether the dialogue text contains any intent, if yes, executing step S4, if not, terminating the process, if impossible to judge, executing step S3.
[0089] A dialogue text with intent differs from a dialogue text without intent relatively greatly in terms of wording and sentence pattern, it can hence be attempted to directly employ some template matching modes to judge whether the user dialogue is a dialogue with intent or a dialogue without intent.
[0090] The specific implementation process of judging whether the dialogue text contains any intent in step S2 can include:
[0091] searching in the dialogue text whether there is any word group matching a preset template, if yes, deciding that the dialogue text contains intent, if not, deciding that the dialogue text contains no intent, wherein the preset template can be embodied as a regular expression mode.
[0092] Besides, since the text expressed by the user in the customer service robot may be a dialogue text of only several terms, this renders the user expression extremely ambiguous and unclear, when it is impossible to judge whether the user dialogue contains any intent by means of the process of step S2, it is required to contextually expand the dialogue text.
[0093] S3 ¨ contextually expanding the dialogue text, and executing step S4 after step S3.
[0094] With reference to what is shown in Fig. 3, the specific implementation process of step S3 can include the following.
[0095] S31 - storing user conversation information with one session as a unit, associating with contextual information of the dialogue text, and judging whether the user intent is Date Regue/Date Received 2022-09-23 changed, wherein the contextual information includes an intent identifying result of context of the dialogue text.
[0096] Specifically, with respect to a dialogue text difficult to judge whether it contains any intent, it is possible to associate with relevant information of the context with one session as a unit, employ conversation information stored in one session to merge plural dialogue texts previously input by the user, and judge whether the intent is changed.
[0097] S32 - employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
[0098] Specifically, keywords in the context are extracted to obtain a set of near-synonyms, and the set of near-synonyms is used to expand the dialogue text.
[0099] In this embodiment, the dialogue text impossible to judge whether it contains any intent is contextually expanded, whereby intent information in the dialogue context can be enriched to facilitate subsequent accurate recognition of user intent.
[0100] S4 ¨ identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set.
[0101] With reference to what is shown in Fig. 4, the specific implementation process of step S4 can include the following.
[0102] S41 - performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms.
[0103] Specifically, a preset term-segmenting mode is employed to perform a term-segmenting process on the dialogue text according to a preset dictionary to obtain a plurality of Date Regue/Date Received 2022-09-23 characters or character sequences, and characters or character strings with practical semantics are screened out of the character sequences as obtained to serve as a term-segmenting result. The preset term-segmenting mode can be to a term-segmenting mode that is based on character matching, based on sematic understanding, or based on statistics.
[0104] S42 - matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set.
[0105] Specifically, with respect to each segmented term in plural segmented terms, a matching degree between each named entity in the entity lexicon and this segmented term is calculated, and any named entity whose matching degree is greater than a preset threshold is taken to serve as the named entity matching this segmented term. In addition, it is possible to employ similarities based on Hamming distance to calculate the matching degree between each named entity in the entity lexicon and this segmented term.
[0106] For example, with respect to segmented terms "Shanghai" and "aged 60"
in a user dialogue text, a named entity "region" of "Shanghai" and a named entity "age"
of "aged 60" can be matched and obtained from the entity lexicon.
in a user dialogue text, a named entity "region" of "Shanghai" and a named entity "age"
of "aged 60" can be matched and obtained from the entity lexicon.
[0107] S43 - determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
[0108] In this embodiment, plural entities correspond to one intent knowledge point, and the intent knowledge point is used to indicate intent information; normalized intent knowledge points can be collected and sorted in advance according to historically accumulated chat data carried out between customer service and users, corresponding plural entities are then determined for each intent knowledge point, and preliminary prediction of the user intent can be obtained by matching with the entity lexicon.
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0109] Specifically, relevancy between the named entity set and each intent knowledge point in the knowledge base is calculated, and any intent knowledge point in the knowledge base associated with the named entity set is determined.
[0110] S5 ¨ expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information.
[0111] With reference to what is shown in Fig. 5, the specific implementation process of step S5 can include the following.
[0112] S51 - performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms.
[0113] Specifically, the specific process of this step is identical with step S41, so it is not redundantly described here.
[0114] S52 - calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution.
[0115] Specifically, word vectors to which term units correspond can be obtained through a Word2Vec model, and the word vectors are expressed in distribution.
[0116] Word2Vec is a specific means of word embedding of natural language processing (NLP), it can characterize semantic information of terms in the mode of word vectors through the learning of texts, i.e., semantically close words are arranged very close in distance within an embedded space through this embedded (low-dimensional) space.
[0117] S53 - inputting the word vectors of the plural segmented terms expressed in distribution Date Regue/Date Received 2022-09-23 to the plural semantically classifying models to output the plural pieces of semantic information.
[0118] The process of training the plural semantically classifying models in step S5 includes the following.
[0119] Q&A data is obtained from the database, the Q&A data is preprocessed, and the preprocessed Q&A data is marked.
[0120] The Q&A data includes information of Q&A pairs accumulated by field human customer service in answering questions raised by users.
[0121] Specifically, the Q&A data can be preprocessed by means of keyword extraction and template rules, partial data without intent is filtered away, and the preprocessed Q&A data is semantically marked by marking personnel.
[0122] For instance, semantic classifications within the field can be particularized into variegated classifications including telephone charges, gift cards, managements of money matters, and Change Treasure, etc., and the Q&A data is marked in advance by marking personnel.
[0123] The marked Q&A data is divided into a training set and a testing set by the mode of offline pretraining.
[0124] Q&A statements in the training set are expressed by distribution of word vectors and are trained in a deep neural network, the testing set is used to test the trained deep neural network, and semantically classifying models whose prediction precisions satisfy a precision threshold are constructed.
[0125] The plural semantically classifying models can be embodied as such various deep-Date Regue/Date Received 2022-09-23 learning semantically classifying models as TextCNN, RNN, LSTM, and CAPsNet, etc., as can be understood by persons skilled in the art, the model training strategy can be a conventional strategy of the corresponding network, to which no explanation is made in this context.
[0126] After deep neural networks have been trained by means of the training set, the testing set can be used to respectively test the plural trained deep neural networks, to appraise prediction precision rates of the deep neural networks, and to adjust network parameters of the deep neural networks in accordance with model prediction precision rate, so as to construct the semantically classifying models whose prediction precisions satisfy the precision threshold.
[0127] In this embodiment of the present invention, semantic association among terms is taken into full consideration while features are being extracted by expressing Q&A
data well marked with semantic classifications by distribution of word vectors and by deep feature mining through deep learning models, to obtain the semantically classifying models. It is hence possible to use the plural semantically classifying models to quickly and accurately predict semantic information with respect to the user dialogue text expressed by distribution of word vectors.
data well marked with semantic classifications by distribution of word vectors and by deep feature mining through deep learning models, to obtain the semantically classifying models. It is hence possible to use the plural semantically classifying models to quickly and accurately predict semantic information with respect to the user dialogue text expressed by distribution of word vectors.
[0128] S6 ¨ employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
[0129] Specifically, the final user intent is determined through the Ensemble framework according to the intent knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
[0130] The basic conception of the Ensemble framework is to form a strong classification framework by taking full advantages of different classification algorithms and Date Regue/Date Received 2022-09-23 compensating one's disadvantage by another's advantage. Plural classifiers are merged together to realize the optimum combination.
[0131] In the intent identifying method for application to an intelligent customer service robot provided by the embodiments of the present invention, when it is impossible to judge whether the dialogue text contains any intent, intent information can be complemented in the user dialogue text by contextually expanding the dialogue text; semantic association among terms is taken into full consideration while features are being extracted by expressing the dialogue text in distributed word vectors and by deep feature mining through a deep learning model; an Ensemble framework is employed to merge and tune the entity matching result and the semantically predicting result to obtain the user intent, whereby are achieved to more quickly and accurately identify the user intent, to enhance precision rate in identification of the user intent, and to reduce error and incompleteness in identification of the user intent, so that guarantee is supplied for the customer service robot to correctly answer questions raised by users.
[0132] Embodiment 2
[0133] As realization of the intent identifying method for application to an intelligent customer service robot in Embodiment 1, this embodiment of the present invention provides an intent identifying device for application to an intelligent customer service robot, with reference to what is shown in Fig. 6, the device comprises:
[0134] a text obtaining module 60, for obtaining a dialogue text of a user;
[0135] an intent judging module 62, for judging whether the dialogue text contains any intent, if yes, executing a process of an entity matching module, if not, terminating the process, if impossible to judge, executing a process of a text expanding module 63;
[0136] the text expanding module 63, for contextually expanding the dialogue text, and executing the process of the entity matching module with respect to the expanded dialogue text;
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0137] the entity matching module 64, for identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set;
[0138] a semantically predicting module 65, for expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information; and
[0139] a merging and tuning module 66, for employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
[0140] Moreover, the device further comprises:
[0141] a text rectifying module 61, for performing text rectification on the dialogue text.
[0142] Further, the text rectifying module 61 is specifically employed for:
[0143] term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text;
[0144] obtaining a rectifying term to which the erroneous segmented term corresponds; and
[0145] replacing the erroneous segmented term in the dialogue text with the rectifying term.
[0146] Further, the text expanding module 63 is specifically employed for:
[0147] storing user conversation information with one session as a unit;
[0148] associating with contextual information of the dialogue text, and judging whether the user intent is changed, wherein the contextual information includes an intent identifying result of context of the dialogue text; and
[0149] employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
[0150] Further, the entity matching module 64 is specifically employed for:
[0151] performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms;
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0152] matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set; and
[0153] determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
[0154] Further, the semantically predicting module 65 is specifically employed for:
[0155] performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms;
[0156] calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution; and
[0157] inputting the word vectors of the plural segmented terms expressed in distribution to the plural semantically classifying models to output the plural pieces of semantic information.
[0158] Further, the merging and tuning module 66 is specifically employed for:
[0159] determining a final user intent through the Ensemble framework according to the intent knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
[0160] The intent identifying device for application to an intelligent customer service robot provided by this embodiment pertains to the same inventive concept as the intent identifying method for application to an intelligent customer service robot provided by the foregoing embodiment of the present invention, can execute the intent identifying method for application to an intelligent customer service robot provided by any embodiment of the present invention, and possesses functional modules and achieves advantageous effects to which the intent identifying method for application to an intelligent customer service robot corresponds. Technical details not particularized in this embodiment can be inferred from the intent identifying method for application to an intelligent customer service robot provided by the foregoing embodiment of the present invention, and are not redundantly described in this context.
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
[0161] All the above optional technical solutions can be randomly combined to form optional embodiments of the present invention, and these are not redundantly described on a one-by-one basis.
[0162] As understandable by persons ordinarily skilled in the art, realization of the entire or partial steps of the aforementioned embodiments can be completed by hardware, or by a program instructing relevant hardware, the program can be stored in a computer-readable storage medium, and the storage medium can be a read-only memory, a magnetic disk, or an optical disk, etc.
[0163] What is described above is merely directed to preferred embodiments of the present invention, and is not meant to restrict the present invention. Any modification, equivalent substitution, and improvement makeable within the spirit and principle of the present invention shall all be covered by the protection scope of the present invention.
Date Regue/Date Received 2022-09-23
Date Regue/Date Received 2022-09-23
Claims (14)
1. An intent identifying method for application to an intelligent customer service robot, characterized in comprising the following steps:
SO ¨ obtaining a dialogue text of a user;
S2 ¨ judging whether the dialogue text contains any intent, if yes, executing step S4, if not, terminating the process, if impossible to judge, executing step S3;
S3 ¨ contextually expanding the dialogue text, and executing step S4 after step S3;
S4 ¨ identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set;
S5 ¨ expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information;
and S6 ¨ employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
SO ¨ obtaining a dialogue text of a user;
S2 ¨ judging whether the dialogue text contains any intent, if yes, executing step S4, if not, terminating the process, if impossible to judge, executing step S3;
S3 ¨ contextually expanding the dialogue text, and executing step S4 after step S3;
S4 ¨ identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set;
S5 ¨ expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information;
and S6 ¨ employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
2. The method according to Claim 1, characterized in that, prior to step S2, the method further comprises the following step:
S1 ¨ performing text rectification on the dialogue text.
S1 ¨ performing text rectification on the dialogue text.
3. The method according to Claim 2, characterized in that step S1 specifically includes:
term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text;
obtaining a rectifying term to which the erroneous segmented term corresponds;
and replacing the erroneous segmented term in the dialogue text with the rectifying term.
term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text;
obtaining a rectifying term to which the erroneous segmented term corresponds;
and replacing the erroneous segmented term in the dialogue text with the rectifying term.
4. The method according to any of Claims 1 to 3, characterized in that step S3 specifically Date Regue/Date Received 2022-09-23 includes:
storing user conversation information with one session as a unit;
associating with contextual information of the dialogue text, and judging whether the user intent is changed, wherein the contextual information includes an intent identifying result of context of the dialogue text; and employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
storing user conversation information with one session as a unit;
associating with contextual information of the dialogue text, and judging whether the user intent is changed, wherein the contextual information includes an intent identifying result of context of the dialogue text; and employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
5. The method according to any of Claims 1 to 3, characterized in that step S4 specifically includes:
performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms;
matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set; and determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms;
matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set; and determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
6. The method according to any of Claims 1 to 3, characterized in that step S5 specifically includes:
performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms;
calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution; and inputting the word vectors of the plural segmented terms expressed in distribution to the plural semantically classifying models to output the plural pieces of semantic information.
performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms;
calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution; and inputting the word vectors of the plural segmented terms expressed in distribution to the plural semantically classifying models to output the plural pieces of semantic information.
7. The method according to any of Claims 1 to 3, characterized in that step S6 specifically includes:
determining a final user intent through the Ensemble framework according to the intent Date Regue/Date Received 2022-09-23 knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
determining a final user intent through the Ensemble framework according to the intent Date Regue/Date Received 2022-09-23 knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
8. An intent identifying device for application to an intelligent customer service robot, characterized in comprising:
a text obtaining module, for obtaining a dialogue text of a user;
an intent judging module, for judging whether the dialogue text contains any intent, if yes, executing a process of an entity matching module, if not, terminating the process, if impossible to judge, executing a process of a text expanding module;
the text expanding module, for contextually expanding the dialogue text, and executing the process of the entity matching module with respect to the expanded dialogue text;
the entity matching module, for identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set;
a semantically predicting module, for expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information; and a merging and tuning module, for employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
a text obtaining module, for obtaining a dialogue text of a user;
an intent judging module, for judging whether the dialogue text contains any intent, if yes, executing a process of an entity matching module, if not, terminating the process, if impossible to judge, executing a process of a text expanding module;
the text expanding module, for contextually expanding the dialogue text, and executing the process of the entity matching module with respect to the expanded dialogue text;
the entity matching module, for identifying a named entity set in the dialogue text, and determining any intent knowledge point associated with the named entity set;
a semantically predicting module, for expressing the dialogue text in distributed word vectors, and employing plural pre-trained semantically classifying models for prediction to obtain plural pieces of semantic information; and a merging and tuning module, for employing an Ensemble framework to merge and tune the intent knowledge point and the plural pieces of semantic information, and obtaining a user intent.
9. The device according to Claim 8, characterized in that the device further comprises:
a text rectifying module, for performing text rectification on the dialogue text.
a text rectifying module, for performing text rectification on the dialogue text.
10. The device according to Claim 9, characterized in that the text rectifying module is specifically employed for:
term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text;
obtaining a rectifying term to which the erroneous segmented term corresponds;
and replacing the erroneous segmented term in the dialogue text with the rectifying term.
Date Regue/Date Received 2022-09-23
term-segmenting the dialogue text, and identifying any erroneous segmented term in the dialogue text;
obtaining a rectifying term to which the erroneous segmented term corresponds;
and replacing the erroneous segmented term in the dialogue text with the rectifying term.
Date Regue/Date Received 2022-09-23
11. The device according to any of Claims 8 to 10, characterized in that the text expanding module is specifically employed for:
storing user conversation information with one session as a unit;
associating with contextual information of the dialogue text, and judging whether the user intent is changed, wherein the contextual information includes an intent identifying result of context of the dialogue text; and employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
storing user conversation information with one session as a unit;
associating with contextual information of the dialogue text, and judging whether the user intent is changed, wherein the contextual information includes an intent identifying result of context of the dialogue text; and employing a near-synonym of the context to expand the dialogue text when the user intent is not changed.
12. The device according to any of Claims 8 to 10, characterized in that the entity matching module is specifically employed for:
performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms;
matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set; and determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
performing a term-segmenting process on the dialogue text according to a preset dictionary, and obtaining plural segmented terms;
matching the plural segmented terms with a preset entity lexicon, and obtaining the named entity set; and determining an intent knowledge point relevant to the named entity set from a preset knowledge base.
13. The device according to anyone of Claims 8 to 10, characterized in that the semantically predicting module is specifically employed for:
performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms;
calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution; and inputting the word vectors of the plural segmented terms expressed in distribution to the plural semantically classifying models to output the plural pieces of semantic information.
performing a term-segmenting process on the dialogue text, and obtaining plural segmented terms;
calculating word vectors of the plural segmented terms, and expressing the word vectors of the plural segmented terms in distribution; and inputting the word vectors of the plural segmented terms expressed in distribution to the plural semantically classifying models to output the plural pieces of semantic information.
14. The device according to any of Claims 8 to 10, characterized in that the merging and tuning Date Regue/Date Received 2022-09-23 module is specifically employed for:
determining a final user intent through the Ensemble framework according to the intent knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
Date Regue/Date Received 2022-09-23
determining a final user intent through the Ensemble framework according to the intent knowledge point, the plural pieces of semantic information, and preset weights to which the plural pieces of semantic information respectively correspond.
Date Regue/Date Received 2022-09-23
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