1. An information extraction method, comprising:

acquiring a text to be extracted;

obtaining a sample set, wherein the sample set comprises a plurality of sample texts and a label of each sample character in the plurality of sample texts;

determining a prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set;

and extracting characters meeting preset requirements from the text to be extracted according to the prediction label of each character, and taking the extracted characters as an extraction result of the text to be extracted.

2. The method of claim 1, wherein the obtaining a sample set comprises: a pre-constructed sample set is obtained.

3. The method of claim 1, wherein the determining the predictive label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set comprises:

for each character in the text to be extracted, calculating the similarity between the character and each sample character in the sample set according to the semantic feature vector of the character and the semantic feature vector of each sample character in the sample set;

and taking the label of the sample character with the highest similarity with the character as the prediction label of the character.

4. The method of claim 1, wherein generating a semantic feature vector for each character in the text to be extracted comprises:

acquiring a field name to be extracted;

splicing the text to be extracted and the field name to be extracted to obtain a word vector, a sentence pair vector and a position vector of each character in a splicing result;

and generating a semantic feature vector of each character in the text to be extracted according to the word vector, the sentence pair vector and the position vector of each character.

5. The method of claim 1, wherein generating a semantic feature vector for each sample character in the sample set comprises:

acquiring a field name to be extracted;

for each sample text in the sample set, splicing the sample text with the field name to be extracted to obtain a word vector, a sentence pair vector and a position vector of each sample character in a splicing result;

and generating a semantic feature vector of each sample character in the sample text according to the word vector, the sentence pair vector and the position vector of each sample character.

6. The method according to claim 1, wherein the extracting characters meeting preset requirements from the text to be extracted according to the prediction label of each character comprises:

generating a prediction label sequence of the text to be extracted according to the prediction label of each character;

determining a label sequence meeting the requirement of a preset label sequence in the predicted label sequence;

and extracting a plurality of characters corresponding to the determined label sequence from the text to be extracted as an extraction result of the text to be extracted.

7. An information extraction apparatus comprising:

the first acquisition unit is used for acquiring a text to be extracted;

the second acquisition unit is used for acquiring a sample set, and the sample set comprises a plurality of sample texts and a label of each sample character in the plurality of sample texts;

the processing unit is used for determining a prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set;

and the extraction unit is used for extracting characters meeting preset requirements from the text to be extracted according to the prediction label of each character, and the extracted characters are used as the extraction result of the text to be extracted.

8. The apparatus according to claim 7, wherein the second acquiring unit, when acquiring the sample set, specifically performs: a pre-constructed sample set is obtained.

9. The apparatus according to claim 7, wherein the processing unit, when determining the prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set, specifically performs:

for each character in the text to be extracted, calculating the similarity between the character and each sample character in the sample set according to the semantic feature vector of the character and the semantic feature vector of each sample character in the sample set;

and taking the label of the sample character with the highest similarity with the character as the prediction label of the character.

10. The apparatus according to claim 7, wherein the processing unit, when generating the semantic feature vector for each character in the text to be extracted, specifically performs:

acquiring a field name to be extracted;

splicing the text to be extracted and the field name to be extracted to obtain a word vector, a sentence pair vector and a position vector of each character in a splicing result;

and generating a semantic feature vector of each character in the text to be extracted according to the word vector, the sentence pair vector and the position vector of each character.

11. The apparatus of claim 7, wherein the processing unit, when generating the semantic feature vector for each sample character in the sample set, specifically performs:

acquiring a field name to be extracted;

for each sample text in the sample set, splicing the sample text with the field name to be extracted to obtain a word vector, a sentence pair vector and a position vector of each sample character in a splicing result;

and generating a semantic feature vector of each sample character in the sample text according to the word vector, the sentence pair vector and the position vector of each sample character.

12. The apparatus according to claim 7, wherein the extraction unit, when extracting characters satisfying preset requirements from the text to be extracted according to the prediction label of each character as the extraction result of the text to be extracted, specifically performs:

generating a prediction label sequence of the text to be extracted according to the prediction label of each character;

determining a label sequence meeting the requirement of a preset label sequence in the predicted label sequence;

and extracting a plurality of characters corresponding to the determined label sequence from the text to be extracted as an extraction result of the text to be extracted.

13. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

Background

The requirement of extracting information generally exists in the daily work of processing documents, for example, when a contract is processed, the information of a party A, a party B, a contract amount and the like in the document needs to be known; when processing legal decision books, information such as "culprit", "prosecutor", "suspect name" in a document needs to be known.

In the prior art, information is usually extracted by using an information extraction model, but the information extraction model only has a good extraction effect on the linguistic data related to the training field of the information extraction model, and the linguistic data outside the training field cannot be accurately extracted due to the lack of corresponding training data. If the extraction capability of the information extraction model in different fields needs to be improved, the most intuitive way is to acquire a large amount of labeled data for training, and the large amount of labeled data needs a large amount of labor cost and is difficult to acquire.

Disclosure of Invention

According to a first aspect of the present disclosure, there is provided an information extraction method, including: acquiring a text to be extracted; obtaining a sample set, wherein the sample set comprises a plurality of sample texts and a label of each sample character in the plurality of sample texts; determining a prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set; and extracting characters meeting preset requirements from the text to be extracted according to the prediction label of each character, and taking the extracted characters as an extraction result of the text to be extracted.

According to a second aspect of the present disclosure, there is provided an information extraction apparatus including: the first acquisition unit is used for acquiring a text to be extracted; the second acquisition unit is used for acquiring a sample set, and the sample set comprises a plurality of sample texts and a label of each sample character in the plurality of sample texts; the processing unit is used for determining a prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set; and the extraction unit is used for extracting characters meeting preset requirements from the text to be extracted according to the prediction label of each character, and the extracted characters are used as the extraction result of the text to be extracted.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.

According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described above.

According to the technical scheme, the prediction label of each character in the text to be extracted is determined through the acquired sample set, and then the character meeting the preset requirement in the text to be extracted is extracted as the extraction result of the text to be extracted, so that the training of an information extraction model is not required, the information extraction steps are simplified, the cost of information extraction is reduced, the field to which the text to be extracted belongs is not limited, the information corresponding to any field name can be extracted from the text to be extracted, and the flexibility and the accuracy of information extraction are greatly improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;

FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;

FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure;

fig. 4 is a block diagram of an electronic device for implementing the information extraction method of the embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure. As shown in fig. 1, the information extraction method of this embodiment may specifically include the following steps:

s101, acquiring a text to be extracted;

s102, obtaining a sample set, wherein the sample set comprises a plurality of sample texts and a label of each sample character in the plurality of sample texts;

s103, determining a prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set;

and S104, extracting characters meeting preset requirements from the text to be extracted according to the prediction label of each character, and taking the extracted characters as the extraction result of the text to be extracted.

According to the information extraction method, the prediction label of each character in the text to be extracted is determined through the acquired sample set, and then the character meeting the preset requirement in the text to be extracted is extracted as the extraction result of the text to be extracted, so that training of an information extraction model is not required, the information extraction steps are simplified, the cost of information extraction is reduced, the field to which the text to be extracted belongs is not limited, information corresponding to any field name can be extracted from the text to be extracted, and the flexibility and the accuracy of information extraction are greatly improved.

The text to be extracted obtained by executing S101 in this embodiment is composed of a plurality of characters, and the domain to which the text to be extracted belongs may be any domain.

In this embodiment, after the S101 is executed to obtain the text to be extracted, the field name to be extracted may be further obtained, where the field name to be extracted includes a text of at least one character, and an extraction result extracted from the text to be extracted is a field value corresponding to the field name to be extracted in the text to be extracted.

For example, if the text to be extracted is "Party A: three, the name of the field to be extracted is "party a", and then the field value "three by three" corresponding to "party a" needs to be extracted from the text to be extracted in this embodiment.

After the step S101 is executed to obtain the text to be extracted, the step S102 is executed to obtain a sample set, where the obtained sample set includes a plurality of sample texts and a label of each sample character in the plurality of sample texts.

In this embodiment, when the step S102 is executed to obtain the sample set, a pre-constructed sample set may be obtained, or a real-time constructed sample set may be obtained. Preferably, in order to improve the efficiency of information extraction, the sample set obtained by performing S102 in the present embodiment is a pre-constructed sample set.

It can be understood that the sample set obtained by performing S102 in this embodiment includes a small number of sample texts, for example, includes a plurality of sample texts within a preset number, where the preset number may be a smaller number, for example, the sample set obtained in this embodiment includes only 5 sample texts.

In the sample set obtained in S102, the present embodiment executes that the labels of different sample characters correspond to the field names to be extracted, and the label of the sample character is used to indicate whether the sample character is the beginning of the field value, the middle of the field value, or whether the sample character is a non-field value.

In the sample set obtained in step S102, the label of each sample character may be one of B, I and O; wherein, the sample character labeled B indicates that the sample character is the beginning of a field value, the sample character labeled I indicates that the sample character is the middle of a field value, and the sample character labeled O indicates that the sample character is a non-field value.

For example, if the sample set in this embodiment includes a sample text of "first party: lee, if the field to be extracted in this embodiment is named "party a", the label of each sample character in the sample text may be "O, B, I", respectively.

After executing S102 to obtain the sample set, executing S103 to determine a prediction tag of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set.

Specifically, when S103 is executed to determine the prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set, the embodiment may adopt an optional implementation manner as follows: aiming at each character in the text to be extracted, calculating the similarity between the character and each sample character in the sample set according to the semantic feature vector of the character and the semantic feature vector of each sample character in the sample set; and taking the label of the sample character with the highest similarity with the character as the prediction label of the character.

That is to say, in the present embodiment, the similarity between the characters in the text to be extracted and the sample characters in the sample set is calculated through the semantic feature vector, so that the label of the sample character having the highest similarity with the characters in the text to be extracted is used as the prediction label of the characters in the text to be extracted, and the accuracy of the determined prediction label is improved.

Alternatively, in the embodiment, when the similarity between the character and the sample character is calculated in S103, the following calculation formula may be adopted:

in the formula:representing the similarity between the ith character and the jth sample character; s_iLanguage indicating ith characterDefining a feature vector; t represents transposition; v_jA semantic feature vector representing the jth sample character.

In the embodiment, when S103 is executed, the semantic feature vector of each character in the text to be extracted or the semantic feature vector of each sample character in the sample text may be generated directly according to the text to be extracted or the sample text itself.

In order to improve the accuracy of the semantic feature vector of each character in the generated text to be extracted, when S103 is executed to generate the semantic feature vector of each character in the text to be extracted, the embodiment may adopt an optional implementation manner as follows: acquiring a field name to be extracted; after splicing the text to be extracted and the field name to be extracted, obtaining a word vector (token embedding), a sentence pair vector (segment embedding) and a position vector (position embedding) of each character in a splicing result, for example, inputting the splicing result into an ERNIE model, and obtaining three vectors output by the ERNIE model aiming at each character; and generating a semantic feature vector of each character in the text to be extracted according to the word vector, the sentence pair vector and the position vector of each character, for example, adding the word vector, the sentence pair vector and the position vector of each character, inputting the result into an ERNIE model, and taking the output result of the ERNIE model as the semantic feature vector of each character.

In order to improve the accuracy of the semantic feature vector of each sample character in the generated sample text, when S103 is executed to generate the semantic feature vector of each sample character in the sample set, the present embodiment may adopt an optional implementation manner as follows: acquiring a field name to be extracted; for each sample text in the sample set, splicing the sample text with the field name to be extracted to obtain a word vector, a sentence pair vector and a position vector of each sample character in a splicing result; and generating a semantic feature vector of each sample character in the sample text according to the word vector, the sentence pair vector and the position vector of each sample character. The method for obtaining the three vectors and the semantic feature vector of each sample character in the sample text in the embodiment is similar to the method for obtaining the three vectors and the semantic feature vector of each character in the text to be extracted.

In this embodiment, when executing S103 to splice the text to be extracted and the field name to be extracted, or splice the sample text and the field name to be extracted, the sample text and the field name to be extracted may be spliced according to a preset splicing rule. Preferably, the concatenation rule of this embodiment is "[ CLS ] field name to be extracted [ SEP ] text to be extracted or sample text [ SEP ]", where [ CLS ] and [ SEP ] are special characters.

For example, if the field name to be extracted in this embodiment is "party a", the sample text is "party a: li IV ", the text to be extracted is' Party A: zhang III ', the obtained splicing result can be ' CLS ' Party A [ SEP ] Party A: liqua [ SEP ] "and" [ CLS ] Party A [ SEP ] Party A: liqua [ SEP ] ".

After the step S103 of determining the prediction label of each character in the text to be extracted is executed, the step S104 of extracting characters meeting preset requirements from the text to be extracted as the extraction result of the text to be extracted is executed according to the prediction label of each character. The preset requirement in this embodiment may be one of a preset tag requirement and a preset tag sequence requirement, and corresponds to the field name to be extracted.

In this embodiment, when S104 is executed to extract, according to the prediction tag of each character, a character meeting the preset requirement from the text to be extracted as an extraction result of the text to be extracted, the characters meeting the preset tag requirement in the text to be extracted may be sequentially determined according to the sequence of the characters, and then the determined characters are extracted to form the extraction result.

In addition, when S104 is executed to extract characters meeting preset requirements from the text to be extracted as an extraction result of the text to be extracted according to the prediction label of each character, the embodiment may adopt an optional implementation manner as follows: generating a prediction label sequence of the text to be extracted according to the prediction label of each character; determining a tag sequence which meets the requirement of a preset tag sequence in the generated predicted tag sequence; and extracting a plurality of characters corresponding to the determined label sequence from the text to be extracted as an extraction result.

For example, if the field to be extracted in this embodiment is named "party a", the text to be extracted is "party a: and Zhang three', the generated prediction tag sequence is OOOBI, the tag sequence corresponding to the field name to be extracted, namely the first party, is required to be BI, and Zhang three, corresponding to the determined tag sequence, namely the BI, is extracted from the text to be extracted to serve as an extraction result.

That is to say, in the embodiment, by generating the prediction tag sequence, the field value corresponding to the field name to be extracted in the text to be extracted can be quickly determined, and then the determined field value is extracted as the extraction result, thereby further improving the efficiency of information extraction.

Fig. 2 is a schematic diagram according to a second embodiment of the present disclosure. As shown in fig. 3, the present embodiment shows a flowchart of information extraction: after acquiring a text to be extracted, a field name to be extracted and a sample set, extracting features according to the field name to be extracted to respectively obtain a semantic feature vector of each character in the text to be extracted and a semantic feature vector of each sample character in the sample set; performing similarity calculation according to the obtained semantic feature vector, thereby determining a prediction label of each character in the text to be extracted; and outputting and decoding according to the prediction label of each character, and further taking the decoding result as the extraction result of the text to be extracted.

Fig. 3 is a schematic diagram according to a third embodiment of the present disclosure. As shown in fig. 3, the information extraction apparatus 300 of the present embodiment may include:

the first obtaining unit 301 is configured to obtain a text to be extracted;

a second obtaining unit 302, configured to obtain a sample set, where the sample set includes a plurality of sample texts and a label of each sample character in the plurality of sample texts;

the processing unit 303 is configured to determine a prediction tag of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set;

the extracting unit 304 is configured to extract, according to the prediction label of each character, a character that meets a preset requirement from the text to be extracted as an extraction result of the text to be extracted.

The text to be extracted acquired by the first acquiring unit 301 is composed of a plurality of characters, and the domain to which the text to be extracted belongs may be any domain.

After acquiring the text to be extracted, the first acquiring unit 301 may further acquire a field name to be extracted, where the field name to be extracted includes a text of at least one character, and an extraction result extracted from the text to be extracted is a field value corresponding to the field name to be extracted in the text to be extracted.

In the embodiment, after the first obtaining unit 301 obtains the text to be extracted, the second obtaining unit 302 obtains a sample set, where the obtained sample set includes a plurality of sample texts and a label of each sample character in the plurality of sample texts.

The second obtaining unit 302 may obtain a pre-constructed sample set or a real-time constructed sample set when obtaining the sample set. Preferably, in order to improve the efficiency of information extraction, the sample set acquired by the second acquiring unit 302 is a pre-constructed sample set.

The sample set obtained by the second obtaining unit 302 contains a small amount of sample texts, for example, contains a plurality of sample texts within a preset number, where the preset number may be a smaller number, for example, the sample set obtained by the second obtaining unit 302 contains only 5 sample texts.

In the sample set acquired by the second acquiring unit 302, labels of different sample characters correspond to field names to be extracted, and the label of a sample character is used for indicating whether the sample character is the beginning of a field value, the middle of the field value or whether the sample character is a non-field value.

In the sample set obtained by the second obtaining unit 302, the label of each sample character may be one of B, I and O; wherein, the sample character labeled B indicates that the sample character is the beginning of a field value, the sample character labeled I indicates that the sample character is the middle of a field value, and the sample character labeled O indicates that the sample character is a non-field value.

After the sample set is obtained by the second obtaining unit 302, the processing unit 303 determines a prediction tag of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each sample character in the sample set.

Specifically, when the processing unit 303 determines the prediction label of each character in the text to be extracted according to the semantic feature vector of each character in the text to be extracted and the semantic feature vector of each character in the sample set, the optional implementation manner that can be adopted is as follows: aiming at each character in the text to be extracted, calculating the similarity between the character and each sample character in the sample set according to the semantic feature vector of the character and the semantic feature vector of each sample character in the sample set; and taking the label of the sample character with the highest similarity with the character as the prediction label of the character.

That is to say, in the present embodiment, the similarity between the characters in the text to be extracted and the sample characters in the sample set is calculated through the semantic feature vector, so that the label of the sample character having the highest similarity with the characters in the text to be extracted is used as the prediction label of the characters in the text to be extracted, and the accuracy of the determined prediction label is improved.

The processing unit 303 may generate a semantic feature vector of each character in the text to be extracted or a semantic feature vector of each sample character in the sample text, respectively, directly according to the text to be extracted itself or the sample text itself.

In order to improve the accuracy of the semantic feature vector of each character in the generated text to be extracted, when the processing unit 303 generates the semantic feature vector of each character in the text to be extracted, the optional implementation manner that can be adopted is as follows: acquiring a field name to be extracted; splicing the text to be extracted and the field name to be extracted to obtain a word vector, a sentence pair vector and a position vector of each character in a splicing result; and generating a semantic feature vector of each character in the text to be extracted according to the word vector, the sentence pair vector and the position vector of each character.

In order to improve the accuracy of the semantic feature vector of each sample character in the generated sample text, when the processing unit 303 generates the semantic feature vector of each sample character in the sample set, the optional implementation manners that can be adopted are: acquiring a field name to be extracted; for each sample text in the sample set, splicing the sample text with the field name to be extracted to obtain a word vector, a sentence pair vector and a position vector of each sample character in a splicing result; and generating a semantic feature vector of each sample character in the sample text according to the word vector, the sentence pair vector and the position vector of each sample character. The method of the processing unit 303 when obtaining the three vectors of each sample character in the sample text and the semantic feature vector is similar to the method when obtaining the three vectors of each character in the text to be extracted and the semantic feature vector.

The processing unit 303 may splice the text to be extracted and the field name to be extracted, or splice the sample text and the field name to be extracted according to a preset splicing rule. Preferably, the concatenation rule in the processing unit 303 is "[ CLS ] field name to be extracted [ SEP ] text to be extracted or sample text [ SEP ]", where [ CLS ] and [ SEP ] are special characters.

After the processing unit 303 determines the prediction label of each character in the text to be extracted, the extraction unit 304 extracts characters meeting preset requirements from the text to be extracted as the extraction result of the text to be extracted according to the prediction label of each character. The preset requirement in the extraction unit 304 may be one of a preset tag requirement and a preset tag sequence requirement, and corresponds to the field name to be extracted.

When extracting characters meeting preset requirements from the text to be extracted as the extraction result of the text to be extracted according to the prediction label of each character, the extraction unit 304 may sequentially determine the characters meeting the preset label requirements in the text to be extracted according to the sequence of the characters, and further extract the determined characters to form the extraction result.

In addition, when extracting characters meeting preset requirements from the text to be extracted as an extraction result of the text to be extracted according to the prediction label of each character, the extraction unit 304 may adopt an optional implementation manner as follows: generating a prediction label sequence of the text to be extracted according to the prediction label of each character; determining a tag sequence which meets the requirement of a preset tag sequence in the generated predicted tag sequence; and extracting a plurality of characters corresponding to the determined label sequence from the text to be extracted as an extraction result.

That is to say, in the embodiment, by generating the prediction tag sequence, the field value corresponding to the field name to be extracted in the text to be extracted can be quickly determined, and then the determined field value is extracted as the extraction result, thereby further improving the efficiency of information extraction.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

As shown in fig. 4, is a block diagram of an electronic device of an information extraction method according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM402, and RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 401 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 401 executes the respective methods and processes described above, such as the information extraction method. For example, in some embodiments, the information extraction method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 408.

In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM402 and/or the communication unit 409. When the computer program is loaded into RAM403 and executed by computing unit 401, one or more steps of the information extraction method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the information extraction method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.