CN111428011A - Word recommendation method, device, equipment and storage medium - Google Patents
Word recommendation method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses a word recommendation method, a word recommendation device, word recommendation equipment and a storage medium. The method comprises the following steps: acquiring at least one round of conversation between a user and a machine; extracting at least one keyword in the at least one round of conversation; and searching a target word in a preset tree-shaped library according to the at least one keyword, and pushing the target word to a user. According to the word recommending method disclosed by the embodiment, keywords in the sentence are obtained based on multiple rounds of conversations between the user and the machine, the target word is searched in the preset database according to the keywords and pushed to the user, and the word recommending accuracy is improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of data processing, in particular to a word recommendation method, a word recommendation device, word recommendation equipment and a storage medium.
Background
In daily life, when a user wants to describe a current situation or a certain phenomenon by using a word, an accurate word cannot be immediately thought due to the limitation of vocabulary amount and the like. It is common practice to input description information into search software for searching, and then the retrieved words are not intended by the user due to the incompleteness of the description information. Not only the retrieved words are not highly accurate and influence the user experience, but also the process of manual retrieval by the user wastes time.
Disclosure of Invention
The embodiment of the disclosure provides a word recommendation method, a word recommendation device, word recommendation equipment and a storage medium, so as to realize word recommendation and improve the accuracy of word recommendation.
In a first aspect, an embodiment of the present disclosure provides a word recommendation method, including:
acquiring at least one round of conversation between a user and a machine;
extracting at least one keyword in the at least one round of conversation;
and searching a target word in a preset tree-shaped library according to the at least one keyword, and pushing the target word to a user.
Further, extracting at least one keyword in the at least one round of dialog comprises:
obtaining a sentence template corresponding to a sentence spoken by a user in the at least one pair of dialogs;
and determining keywords respectively corresponding to each sentence spoken by the user according to the sentence template to obtain at least one keyword.
Further, the sentence template is composed of a plurality of word slots, each sentence template specifies that words in the word slots are set as keywords of the current sentence, and the keywords corresponding to each sentence spoken by the user are determined according to the sentence template, including:
segmenting the current sentence spoken by the user according to the sentence pattern and/or the sentence pattern to obtain at least one word;
and filling the at least one word into a word slot of a sentence template corresponding to the current sentence respectively to obtain a keyword corresponding to the current sentence.
Further, before acquiring at least one pair of conversations between the user and the machine, the method further comprises:
collecting Chinese word sets, and adding a label to each word in the word sets according to word senses;
and carrying out grade division on the word set according to the label to obtain a preset tree-shaped library.
Further, searching a target word in a preset tree-shaped library according to the at least one keyword, including:
if the number of the keywords is multiple, ranking the multiple keywords;
and searching step by step in a preset tree-shaped library according to the sorted keywords to obtain the target words.
Further, before acquiring multiple rounds of conversations between the user and the machine, comprising:
collecting voice information input by a user, and performing semantic recognition on the voice information to obtain semantic information;
determining feedback information according to the semantic information;
acquiring new voice information input by a user according to the feedback information, and performing semantic recognition on the new voice information to obtain new semantic information;
and judging whether a command for stopping the conversation is detected, if not, determining new feedback information according to the new semantic information, and returning to execute the operation of collecting new voice information input by the user according to the feedback information until the command for stopping the conversation is detected.
Further, searching the target words in a preset tree-shaped library according to the at least one keyword comprises:
acquiring a limiting condition of the target word;
and filtering the target words according to the limiting conditions.
In a second aspect, an embodiment of the present disclosure further provides a word recommendation apparatus, including:
the conversation acquisition module is used for acquiring at least one round of conversation between a user and the machine;
the keyword acquisition module is used for extracting at least one keyword in the at least one round of conversation;
the target word pushing module is used for searching a target word in a preset tree-shaped library according to the at least one keyword; and pushing the target words to the user.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
one or more processing devices;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processing devices, the one or more processing devices are caused to implement the word recommendation method according to the embodiment of the present disclosure.
In a fourth aspect, the disclosed embodiments also provide a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processing device, implements a method for recommending words as described in the disclosed embodiments.
According to the embodiment of the disclosure, at least one round of conversation between a user and a machine is firstly obtained, at least one keyword in the at least one round of conversation is then extracted, and finally, a target word is searched in a preset tree-shaped library according to the at least one keyword, and the target word is pushed to the user. According to the word recommending method disclosed by the embodiment, keywords in the sentence are obtained based on multiple rounds of conversations between the user and the machine, the target word is searched in the preset database according to the keywords and pushed to the user, and the word recommending accuracy is improved.
Drawings
FIG. 1 is a flow chart of a word recommendation method in a first embodiment of the disclosure;
FIG. 2 is a schematic structural diagram of a word recommendation device in a second embodiment of the disclosure;
fig. 3 is a schematic structural diagram of an electronic device in a third embodiment of the disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the disclosure and are not limiting of the disclosure. It should be further noted that, for the convenience of description, only some of the structures relevant to the present disclosure are shown in the drawings, not all of them.
In the following embodiments, optional features and examples are provided in each embodiment, and various features described in the embodiments may be combined to form a plurality of alternatives, and each numbered embodiment should not be regarded as only one technical solution.
Example one
Fig. 1 is a flowchart of a word recommendation method provided in an embodiment of the present disclosure, where this embodiment is applicable to a word recommendation situation, and the method may be executed by a word recommendation apparatus, where the apparatus may be composed of hardware and/or software, and may be generally integrated in a device having a word recommendation function, where the device may be an electronic device such as a server, a mobile terminal, or a server cluster. As shown in fig. 1, the method specifically includes the following steps:
at least one round of dialog between the user and the machine is obtained, step 110.
In this embodiment, when a user needs a word to describe a current situation or a certain phenomenon, at least one round of conversation with the machine is performed to describe a purpose, paraphrase, or related word, etc. In this embodiment, the process of acquiring at least one round of dialog between the user and the machine may be: collecting voice information input by a user, and performing semantic recognition on the voice information to obtain semantic information; determining feedback information according to the semantic information; acquiring new voice information input by a user according to the feedback information, and performing semantic recognition on the new voice information to obtain new semantic information; and judging whether a command for stopping the conversation is detected, if not, determining new feedback information according to the new semantic information, and returning to execute the operation of collecting new voice information input by the user according to the feedback information until the command for stopping the conversation is detected.
The manner of detecting the instruction to stop the dialog may be that voice information input by the user is not collected within a set time, and the set time may be any value between 5 and 10 seconds. Specifically, a user inputs description or paraphrase about a target word through voice, the machine performs semantic recognition on collected voice information, whether feedback information is generated or not is judged according to the semantic information, namely whether the description needs to be supplemented by the user is judged through the semantic information, if yes, the feedback information is generated, the user inputs supplemented new voice according to the feedback information, and the new voice information is subjected to semantic recognition to obtain new semantic information. If the command for stopping the conversation is not detected, whether the user needs to continue supplementing the description is judged according to the new semantic information until the command for stopping the conversation is detected, and at least one round of conversation between the user and the machine is obtained.
Step 120, at least one keyword in at least one round of conversation is extracted.
In this embodiment, keywords are extracted from sentences spoken by the user in at least one pair of dialogs. Extracting at least one keyword in at least one round of conversation, comprising: obtaining sentence templates corresponding to sentences spoken by a user in at least one pair of conversations; and determining keywords respectively corresponding to each sentence spoken by the user according to the sentence template to obtain at least one keyword.
The sentence template is composed of a plurality of word slots, and each sentence template specifies that the words in the word slots are set as the keywords of the current sentence. The sentence pattern can comprise judgment sentences, passive sentences, prepositive object, composition omission sentences, prepositive object in negative sentences, prepositive object in question sentences, prepositive object in prepositive object, postpositive object and the like, and the sentence pattern can comprise cardinal predicates, non-cardinal predicates, passive sentences, inverted sentences, doublet sentences, linkage sentences and the like. For example, for a decision sentence, the sentence is "the subject-predicate" if "A is B", then the words in the object will be determined as keywords in the decision sentence.
Optionally, determining the keywords corresponding to each sentence spoken by the user according to the sentence template may be implemented in the following manner: segmenting the current sentence spoken by the user according to the sentence pattern and/or the sentence pattern to obtain at least one word; and filling at least one word into a word slot of a sentence template corresponding to the current sentence respectively to obtain a keyword corresponding to the current sentence.
Specifically, after the sentence spoken by the user is divided into at least one word according to the sentence pattern and the sentence pattern, the at least one word obtained by the division is filled into the word slot of the corresponding sentence template, so as to obtain the keyword corresponding to the current sentence. For example, assuming that a sentence pattern of a sentence spoken by a user is a subject predicate, the sentence is divided to obtain words corresponding to the subject, words corresponding to the predicate, and words corresponding to the object, and the three words are filled in word slots in a subject predicate template, and if words located in the subject word slots and the predicate word slots in the subject predicate sentence template are specified as keywords, words corresponding to the subject and the predicate in the sentence are the keywords.
And step 130, searching the target words in a preset tree-shaped library according to at least one keyword, and pushing the target words to the user.
The preset tree-shaped library can be obtained by collecting a Chinese word set and adding a label to each word in the word set according to the word sense; and carrying out grade division on the word set according to the label to obtain a preset tree-shaped library.
The way of collecting chinese words may be from a thesaurus, or an official dictionary. Specifically, at least one tag is added to a word according to the word sense of the word, and the word set may be ranked according to the number of tags, for example: the words with one label are first-level words, the words with two labels are second-level words, and the words with three labels are third-level labels. For example: the label of "running" is "running", which is a first-level word, "thousand miles a day" is "running + fast", which is a second-level word, "swingack" is "running + fast + animal", which is a third-level word.
Searching a target word in a preset tree-shaped library according to at least one keyword, wherein the searching comprises the following steps: if the number of the keywords is multiple, ranking the multiple keywords; and searching step by step in a preset tree-shaped library according to the sorted keywords to obtain the target words.
When a plurality of keywords exist, the keywords are firstly sorted according to the grades, and finally, the keywords after sorting are searched step by step in a preset tree-shaped library to obtain the target words. The ranking of the keywords may be ordered according to the parts of speech of the words, for example: the real word is ranked higher than the particle word, and the ranking from high to low for the real word may be: nouns, verbs, adjectives, quantifiers, and the like, and the levels of the virtual words from high to low can be adverbs, language-atmosphere words, conjunctions, and the like. For keywords with the same part of speech, the rank can be determined according to the word number of the word, and the higher the word number is, the higher the rank is. And if the parts of speech of the two keywords are the same and the number of words is also the same, determining the grade according to the sequence of the first letter. For example, assume that the ordered keywords are: when the user runs, fast runs and resembles a bird, firstly, words related to running are searched according to the running, then words of the fast runs are searched by combining the fast running, and finally, the searching is continued by combining the bird-liked word to obtain the target word of 'walking as if flying'.
Optionally, searching for the target word in the preset tree library according to at least one keyword includes: acquiring a limiting condition of a target word; and filtering the target words according to the defined conditions.
Wherein the limitation condition may be a limitation on the number. And (4) assuming that the limiting condition is 4-word words, filtering out non-four-word words in the target words to obtain the final target words. And after the target words are obtained, pushing the target words to the user.
According to the technical scheme, at least one round of conversation between a user and a machine is obtained, at least one keyword in the round of conversation is extracted, and finally, the target word is searched in the preset tree-shaped library according to the at least one keyword, and the target word is pushed to the user. According to the word recommending method disclosed by the embodiment, keywords in the sentence are obtained based on multiple rounds of conversations between the user and the machine, the target word is searched in the preset database according to the keywords and pushed to the user, and the word recommending accuracy is improved.
Example two
Fig. 2 is a schematic structural diagram of a word recommendation device provided in the second embodiment of the present disclosure. As shown in fig. 2, the apparatus includes: a dialog acquisition module 210, a keyword acquisition module 220, and a target word pushing module 230.
A dialog acquisition module 210 for acquiring at least one round of dialog between a user and a machine;
a keyword obtaining module 220, configured to extract at least one keyword in the at least one round of dialog;
and the target word pushing module 230 is configured to search a target word in a preset tree library according to the at least one keyword, and push the target word to a user.
Optionally, the keyword obtaining module 220 is further configured to:
obtaining a sentence template corresponding to a sentence spoken by a user in the at least one pair of dialogs;
and determining keywords respectively corresponding to each sentence spoken by the user according to the sentence template to obtain at least one keyword.
Optionally, the sentence template is composed of a plurality of word slots, each sentence template specifies that words in the set word slots are keywords of the current sentence, and the keyword obtaining module 220 is further configured to:
segmenting the current sentence spoken by the user according to the sentence pattern and/or the sentence pattern to obtain at least one word;
and filling the at least one word into a word slot of a sentence template corresponding to the current sentence respectively to obtain a keyword corresponding to the current sentence.
Optionally, the method further includes:
the tag adding module is used for collecting the Chinese word set and adding a tag to each word in the word set according to the word sense;
and the preset tree library establishing module is used for carrying out grade division on the word set according to the label to obtain a preset tree library.
Optionally, the target word pushing module 230 is further configured to:
if the number of the keywords is multiple, ranking the multiple keywords;
and searching step by step in a preset tree-shaped library according to the sorted keywords to obtain the target words.
Optionally, the method further includes: a dialog module to:
collecting voice information input by a user, and performing semantic recognition on the voice information to obtain semantic information;
determining feedback information according to the semantic information;
acquiring new voice information input by a user according to the feedback information, and performing semantic recognition on the new voice information to obtain new semantic information;
and judging whether a command for stopping the conversation is detected, if not, determining new feedback information according to the new semantic information, and returning to execute the operation of collecting new voice information input by the user according to the feedback information until the command for stopping the conversation is detected.
Optionally, the target word pushing module 230 is further configured to:
acquiring a limiting condition of the target word;
and filtering the target words according to the limiting conditions.
The device can execute the methods provided by all the embodiments of the disclosure, and has corresponding functional modules and beneficial effects for executing the methods. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in all the foregoing embodiments of the disclosure.
EXAMPLE III
Referring now to FIG. 3, a block diagram of an electronic device 300 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like, or various forms of servers such as a stand-alone server or a server cluster. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, electronic device 300 may include a processing means (e.g., central processing unit, graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a read-only memory device (ROM)302 or a program loaded from a storage device 305 into a random access memory device (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc., output devices 307 including, for example, a liquid crystal display (L CD), speaker, vibrator, etc., storage devices 308 including, for example, magnetic tape, hard disk, etc., and communication devices 309, communication devices 309 may allow electronic apparatus 300 to communicate wirelessly or wiredly with other devices to exchange data.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program containing program code for performing a method for recommending words. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 305, or installed from the ROM 302. The computer program, when executed by the processing device 301, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory device (RAM), a read-only memory device (ROM), an erasable programmable read-only memory device (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory device (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the processing device, cause the electronic device to: acquiring at least one round of conversation between a user and a machine; extracting at least one keyword in at least one round of conversation; and searching a target word in a preset tree-shaped library according to at least one keyword, and pushing the target word to a user.
Computer program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the module does not in some cases constitute a limitation of the module itself, for example, the obtaining module may also be described as a module for obtaining an operable control associated with promotion content.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present disclosure and the technical principles employed. Those skilled in the art will appreciate that the present disclosure is not limited to the particular embodiments described herein, and that various obvious changes, adaptations, and substitutions are possible, without departing from the scope of the present disclosure. Therefore, although the present disclosure has been described in greater detail with reference to the above embodiments, the present disclosure is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present disclosure, the scope of which is determined by the scope of the appended claims.
Claims (10)
1. A word recommendation method is characterized by comprising the following steps:
acquiring at least one round of conversation between a user and a machine;
extracting at least one keyword in the at least one round of conversation;
and searching a target word in a preset tree-shaped library according to the at least one keyword, and pushing the target word to a user.
2. The method of claim 1, wherein extracting at least one keyword in the at least one round of dialog comprises:
obtaining a sentence template corresponding to a sentence spoken by a user in the at least one pair of dialogs;
and determining keywords respectively corresponding to each sentence spoken by the user according to the sentence template to obtain at least one keyword.
3. The method of claim 2, wherein the sentence template is composed of a plurality of word slots, each sentence template specifies the words in the word slots as the keywords of the current sentence, and the determining the keywords corresponding to each sentence spoken by the user according to the sentence template comprises:
segmenting the current sentence spoken by the user according to the sentence pattern and/or the sentence pattern to obtain at least one word;
and filling the at least one word into a word slot of a sentence template corresponding to the current sentence respectively to obtain a keyword corresponding to the current sentence.
4. The method of claim 1, further comprising, prior to acquiring at least one pair of conversations between a user and a machine:
collecting Chinese word sets, and adding a label to each word in the word sets according to word senses;
and carrying out grade division on the word set according to the label to obtain a preset tree-shaped library.
5. The method of claim 4, wherein searching for the target word in the predetermined tree library according to the at least one keyword comprises:
if the number of the keywords is multiple, ranking the multiple keywords;
and searching step by step in a preset tree-shaped library according to the sorted keywords to obtain the target words.
6. The method of claim 1, prior to obtaining multiple rounds of dialog between a user and a machine, comprising:
collecting voice information input by a user, and performing semantic recognition on the voice information to obtain semantic information;
determining feedback information according to the semantic information;
acquiring new voice information input by a user according to the feedback information, and performing semantic recognition on the new voice information to obtain new semantic information;
and judging whether a command for stopping the conversation is detected, if not, determining new feedback information according to the new semantic information, and returning to execute the operation of collecting new voice information input by the user according to the feedback information until the command for stopping the conversation is detected.
7. The method of claim 1, wherein searching for a target term in a predetermined tree library according to the at least one keyword comprises:
acquiring a limiting condition of the target word;
and filtering the target words according to the limiting conditions.
8. An apparatus for recommending words, comprising:
the conversation acquisition module is used for acquiring at least one round of conversation between a user and the machine;
the keyword acquisition module is used for extracting at least one keyword in the at least one round of conversation;
and the target word pushing module is used for searching a target word in a preset tree-shaped library according to the at least one keyword and pushing the target word to a user.
9. An electronic device, characterized in that the electronic device comprises:
one or more processing devices;
storage means for storing one or more programs;
when executed by the one or more processing devices, cause the one or more processing devices to implement a method of recommendation of a term as recited in any of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, characterized in that the program, when being executed by processing means, carries out a method for recommending words according to any one of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112905643A (en) * | 2021-03-11 | 2021-06-04 | 广西电力职业技术学院 | Method and system for automatically retrieving from automobile fault case library |
| CN114118101A (en) * | 2021-11-26 | 2022-03-01 | 北京百度网讯科技有限公司 | Dialogue data generation method and device, equipment and medium |
| CN116628140A (en) * | 2023-07-20 | 2023-08-22 | 湖南华菱电子商务有限公司 | Information pushing method and device based on man-machine interaction and man-machine interaction system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103970857A (en) * | 2014-05-07 | 2014-08-06 | 百度在线网络技术（北京）有限公司 | Recommended content determining system and method |
| CN105956206A (en) * | 2016-07-04 | 2016-09-21 | Tcl集团股份有限公司 | Video retrieval method based on keyword tree and video retrieval system based on keyword tree |
| CN108345610A (en) * | 2017-01-24 | 2018-07-31 | 北京搜狗科技发展有限公司 | It is a kind of to obtain the method and apparatus of data resource, the device for obtaining data resource |
-
2019
- 2019-01-10 CN CN201910022936.7A patent/CN111428011B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103970857A (en) * | 2014-05-07 | 2014-08-06 | 百度在线网络技术（北京）有限公司 | Recommended content determining system and method |
| CN105956206A (en) * | 2016-07-04 | 2016-09-21 | Tcl集团股份有限公司 | Video retrieval method based on keyword tree and video retrieval system based on keyword tree |
| CN108345610A (en) * | 2017-01-24 | 2018-07-31 | 北京搜狗科技发展有限公司 | It is a kind of to obtain the method and apparatus of data resource, the device for obtaining data resource |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112905643A (en) * | 2021-03-11 | 2021-06-04 | 广西电力职业技术学院 | Method and system for automatically retrieving from automobile fault case library |
| CN112905643B (en) * | 2021-03-11 | 2022-12-16 | 广西电力职业技术学院 | Method and system for automatically retrieving from automobile fault case library |
| CN114118101A (en) * | 2021-11-26 | 2022-03-01 | 北京百度网讯科技有限公司 | Dialogue data generation method and device, equipment and medium |
| CN116628140A (en) * | 2023-07-20 | 2023-08-22 | 湖南华菱电子商务有限公司 | Information pushing method and device based on man-machine interaction and man-machine interaction system |
| CN116628140B (en) * | 2023-07-20 | 2023-10-27 | 湖南华菱电子商务有限公司 | Information pushing method and device based on man-machine interaction and man-machine interaction system |
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