阅读说明：本技术 一种公告内容分析方法、系统、电子设备及存储介质 (Method, system, electronic device and storage medium for analyzing announcement content ) 是由 朱菁 毛瑞彬 杨雯雯 邓永翠 潘斌强 张大千 尚东东 孙德旺 张俊 杨建明 于 2021-07-21 设计创作，主要内容包括：本申请公开了一种公告内容分析方法,所述公告内容分析方法包括：对目标公告中的元素进行标注得到所述目标公告的篇章结构；根据所述篇章结构提取所述目标公告的元素特征得到每一所述元素的特征工程；根据所述特征工程生成所述目标公告的知识图谱；若接收到公告内容分析请求,则利用所述知识图谱输出所述公告内容分析请求对应的分析结果。本申请能够提高对公告的处理精度,实现高准确性的公告内容分析。本申请还公开了一种公告内容分析系统、一种电子设备及一种存储介质,具有以上有益效果。(The application discloses a method for analyzing announcement content, which comprises the following steps: marking elements in the target bulletin to obtain a chapter structure of the target bulletin; extracting element characteristics of the target bulletin according to the discourse structure to obtain characteristic engineering of each element; generating a knowledge graph of the target notice according to the feature engineering; and if the bulletin content analysis request is received, outputting an analysis result corresponding to the bulletin content analysis request by using the knowledge graph. The method and the device can improve the processing precision of the bulletins and realize the analysis of the bulletin contents with high accuracy. The application also discloses a system for analyzing the announcement content, an electronic device and a storage medium, which have the beneficial effects.)

1. A method for analyzing advertisement content, comprising:

marking elements in the target bulletin to obtain a chapter structure of the target bulletin;

extracting element characteristics of the target bulletin according to the discourse structure to obtain characteristic engineering of each element;

generating a knowledge graph of the target notice according to the feature engineering;

and if the bulletin content analysis request is received, outputting an analysis result corresponding to the bulletin content analysis request by using the knowledge graph.

2. The method for analyzing bulletin contents as claimed in claim 1, wherein the labeling elements in the target bulletin to obtain a chapter structure of the target bulletin comprises:

and labeling the catalog, paragraph, table and custom elements in the target bulletin to obtain the chapter structure of the target bulletin.

3. The method for analyzing the bulletin contents of claim 1, wherein the extracting the element features of the target bulletin according to the discourse structure to obtain the feature engineering of each element comprises:

preprocessing the target bulletin according to the discourse structure to obtain element characteristics; the element features comprise any one item or any combination of several items in element entities, entity relations, keywords, TF-IDF values of the keywords, paragraph position information, text semantic vectors of the paragraphs, and the similarity of the paragraphs and the titles at each level;

and storing the element characteristics according to the corresponding relation between the element characteristics and the elements to obtain the characteristic engineering corresponding to each element.

4. The method for analyzing the announcement content of claim 1, wherein generating the knowledge graph of the target announcement according to the feature engineering comprises:

constructing an announcement content analysis model of the target announcement; the system comprises a bulletin content analysis model, a query analysis module and a query analysis module, wherein the bulletin content analysis model comprises a positioning function module, an extraction function module, a classification function module and a calculation function module, the positioning function module is used for positioning query content, the extraction function module is used for extracting information corresponding to query conditions, the classification function module is used for outputting classification results corresponding to query problems, and the calculation function module is used for calculating calculation results corresponding to the query problems;

inputting the characteristic engineering of each element and adjacent elements into the announcement content analysis model, and training the announcement content analysis model to obtain a model processing result;

and generating the indication map according to the model processing result.

5. The method for analyzing advertisement content according to claim 4, further comprising, during the training of the advertisement content analysis model:

and performing visual processing on the training processes of the positioning function module, the extraction function module, the classification function module and the calculation function module so as to display the positioning result of the positioning function module, the extraction result of the extraction function module, the classification result of the classification function module and the calculation result of the calculation function module on a user interface.

6. The method of claim 5, further comprising, after training the advertisement content analysis model:

and receiving the correction results of the positioning results, the extraction results, the classification results and the calculation results of the user, and performing incremental training on the announcement content analysis model according to the correction results.

7. The method for analyzing the announcement content of claim 1, wherein generating the knowledge graph of the target announcement according to the feature engineering comprises:

constructing four operation rules of a regular expression according to the elements, performing rule matching on the feature engineering by using the four operation rules, and marking a text hitting the four operation rules;

and generating a knowledge graph of the target notice according to the rule matching result.

8. A bulletin content analyzing system, comprising:

the marking module is used for marking elements in the target bulletin to obtain a chapter structure of the target bulletin;

the characteristic extraction module is used for extracting the element characteristics of the target bulletin according to the discourse structure to obtain the characteristic engineering of each element;

the knowledge graph generating module is used for generating a knowledge graph of the target notice according to the characteristic engineering;

and the bulletin analysis module is used for outputting an analysis result corresponding to the bulletin content analysis request by using the knowledge graph if the bulletin content analysis request is received.

9. An electronic device, comprising a memory in which a computer program is stored and a processor, wherein the processor implements the steps of the advertisement content analysis method according to any one of claims 1 to 7 when calling the computer program in the memory.

10. A storage medium having stored thereon computer-executable instructions which, when loaded and executed by a processor, carry out the steps of a method of advertising content analysis as claimed in any one of claims 1 to 7.

Technical Field

The present application relates to the field of text processing technologies, and in particular, to a method, a system, an electronic device, and a storage medium for analyzing advertisement content.

Background

Public announcements of marketing company information are an important source of data in securities investments, and investors rely on analysis and interpretation of data to make investment decisions. The annotation and training process of the bulletin is complex, and the current method mainly comprises the steps of disassembling and then processing the document. The processing precision of the bulletin content analysis method for disassembling the document and then processing the document depends on the specification degree of bulletin, so that the processing precision of the conventional bulletin content analysis scheme is low.

Therefore, how to improve the processing precision of the bulletins and achieve high-accuracy analysis of the bulletin contents is a technical problem that needs to be solved by those skilled in the art at present.

Disclosure of Invention

An object of the present application is to provide a method, a system, a storage medium, and an electronic device for analyzing a content of a bulletin, which can improve processing accuracy of the bulletin and realize analysis of the content of the bulletin with high accuracy.

In order to solve the above technical problem, the present application provides a method for analyzing advertisement content, including:

marking elements in the target bulletin to obtain a chapter structure of the target bulletin;

extracting element characteristics of the target bulletin according to the discourse structure to obtain characteristic engineering of each element;

generating a knowledge graph of the target notice according to the feature engineering;

and if the bulletin content analysis request is received, outputting an analysis result corresponding to the bulletin content analysis request by using the knowledge graph.

Optionally, the labeling the elements in the target advertisement to obtain a chapter structure of the target advertisement includes:

and labeling the catalog, paragraph, table and custom elements in the target bulletin to obtain the chapter structure of the target bulletin.

Optionally, extracting element features of the target advertisement according to the discourse structure to obtain a feature engineering of each element, including:

preprocessing the target bulletin according to the discourse structure to obtain element characteristics; the element features comprise any one item or any combination of several items in element entities, entity relations, keywords, TF-IDF values of the keywords, paragraph position information, text semantic vectors of the paragraphs, and the similarity of the paragraphs and the titles at each level;

and storing the element characteristics according to the corresponding relation between the element characteristics and the elements to obtain the characteristic engineering corresponding to each element.

Optionally, generating a knowledge graph of the target advertisement according to the feature engineering includes:

constructing an announcement content analysis model of the target announcement; the system comprises a bulletin content analysis model, a query analysis module and a query analysis module, wherein the bulletin content analysis model comprises a positioning function module, an extraction function module, a classification function module and a calculation function module, the positioning function module is used for positioning query content, the extraction function module is used for extracting information corresponding to query conditions, the classification function module is used for outputting classification results corresponding to query problems, and the calculation function module is used for calculating calculation results corresponding to the query problems;

inputting the characteristic engineering of each element and adjacent elements into the announcement content analysis model, and training the announcement content analysis model to obtain a model processing result;

and generating the indication map according to the model processing result.

Optionally, in the process of training the advertisement content analysis model, the method further includes:

and performing visual processing on the training processes of the positioning function module, the extraction function module, the classification function module and the calculation function module so as to display the positioning result of the positioning function module, the extraction result of the extraction function module, the classification result of the classification function module and the calculation result of the calculation function module on a user interface.

Optionally, after the training of the advertisement content analysis model, the method further includes:

and receiving the correction results of the positioning results, the extraction results, the classification results and the calculation results of the user, and performing incremental training on the announcement content analysis model according to the correction results.

Optionally, generating a knowledge graph of the target advertisement according to the feature engineering includes:

constructing four operation rules of a regular expression according to the elements, performing rule matching on the feature engineering by using the four operation rules, and marking a text hitting the four operation rules;

and generating a knowledge graph of the target notice according to the rule matching result.

The present application also provides a system for analyzing advertisement content, the system comprising:

the marking module is used for marking elements in the target bulletin to obtain a chapter structure of the target bulletin;

the characteristic extraction module is used for extracting the element characteristics of the target bulletin according to the discourse structure to obtain the characteristic engineering of each element;

the knowledge graph generating module is used for generating a knowledge graph of the target notice according to the characteristic engineering;

and the bulletin analysis module is used for outputting an analysis result corresponding to the bulletin content analysis request by using the knowledge graph if the bulletin content analysis request is received.

The present application also provides a storage medium having stored thereon a computer program that, when executed, performs the steps performed by the above-described announcement content analysis method.

The application also provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps executed by the announcement content analysis method when calling the computer program in the memory.

The application provides a method for analyzing announcement content, which comprises the following steps: marking elements in the target bulletin to obtain a chapter structure of the target bulletin; extracting element characteristics of the target bulletin according to the discourse structure to obtain characteristic engineering of each element; generating a knowledge graph of the target notice according to the feature engineering; and if the bulletin content analysis request is received, outputting an analysis result corresponding to the bulletin content analysis request by using the knowledge graph.

The method and the device have the advantages that the discourse structure is obtained by labeling the elements in the target bulletin, and then the characteristic engineering of each element in the target bulletin is extracted based on the discourse structure. The feature engineering can include feature information of each element, and the knowledge graph of the target notice can be generated by using the feature engineering, and includes all knowledge information in the target notice. After receiving the request for analyzing the announcement content, the knowledge graph can be used for directly outputting an analysis result corresponding to the request for analyzing the announcement content. The knowledge graph is generated based on the chapter structure of the target announcement, and the semantic knowledge of the chapter structure is embedded into the knowledge graph, so that the processing precision of the announcement can be improved, and high-accuracy analysis of the announcement content is realized. The application also provides an announcement content analysis system, an electronic device and a storage medium, which have the beneficial effects and are not repeated herein.

Drawings

In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

Fig. 1 is a flowchart of a method for analyzing advertisement content according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a system for analyzing advertisement content according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Referring to fig. 1, fig. 1 is a flowchart of a method for analyzing advertisement content according to an embodiment of the present application.

The specific steps may include:

s101: marking elements in the target bulletin to obtain a chapter structure of the target bulletin;

the analysis target bulletin in the present embodiment may be a bulletin such as a public bulletin and a public endorsement of a listed company, and the type and content of the bulletin are not limited herein. The target bulletin may include elements such as a directory, a paragraph, and a table, and the embodiment may label the elements in the target bulletin through the element label labeling model to obtain a chapter structure of the target bulletin.

Specifically, the embodiment may label the directory, the paragraph, the table, and the custom element in the target advertisement to obtain the chapter structure of the target advertisement.

S102: extracting element characteristics of the target bulletin according to the discourse structure to obtain characteristic engineering of each element;

after the chapter structure of the target announcement is obtained, the step extracts the element characteristics of the target announcement according to the chapter structure, and takes all the element characteristics corresponding to each element as the characteristic engineering of the element.

Specifically, the feature engineering of each element can be obtained in the following manner in this embodiment: preprocessing the target bulletin according to the discourse structure to obtain element characteristics; and storing the element characteristics according to the corresponding relation between the element characteristics and the elements to obtain the characteristic engineering corresponding to each element.

The element features include any one or a combination of any several of an element entity, an entity relationship, a keyword, a TF-IDF (term frequency-inverse text frequency index) value of the keyword, paragraph position information, a text semantic vector of the paragraph, a similarity of the paragraph and the title of each level. The embodiment may preset an entity table and a keyword table, determine an element entity in the target advertisement based on the entity table, and query keywords in the target advertisement based on the keyword table. For example, entity nouns such as company name, person name, time, place, product, event, attribute, and industry may be included in the entity table. The entity relationship refers to a relationship between entities, for example, there is a text "company a provides raw material for company B" in the target bulletin, when the entity relationship between entity "company a" and entity "company B" is a supplier relationship.

S103: generating a knowledge graph of the target notice according to the feature engineering;

the feature engineering stores elements as units, and the feature engineering contains feature information of each element in the target bulletin, so that semantic knowledge of a target bulletin chapter structure is embedded in a knowledge graph generated according to the feature engineering.

As a feasible implementation manner, the knowledge graph of the target advertisement may be generated by training a machine learning model, and the implementation may also generate the knowledge graph of the target advertisement by constructing four operation rules of a regular expression.

S104: and if the bulletin content analysis request is received, outputting an analysis result corresponding to the bulletin content analysis request by using the knowledge graph.

The knowledge graph is embedded with semantic knowledge of a chapter structure, so that after receiving the announcement content analysis request, the knowledge graph can be subjected to graph calculation to obtain an analysis result corresponding to the announcement content analysis request. Any one or any combination of query content, query conditions and query questions can be obtained by analyzing the advertisement content analysis request. For example, when the announcement content analysis request is a request for determining whether the target announcement has an investment risk, the embodiment may determine whether the target announcement has an investment risk by using a knowledge graph.

In the embodiment, the discourse structure is obtained by labeling the elements in the target bulletin, and the feature engineering of each element in the target bulletin is further extracted based on the discourse structure. The feature engineering may include feature information of each element, and the present embodiment may generate a knowledge graph of the target advertisement by using the feature engineering, where the knowledge graph includes all knowledge information in the target advertisement. After receiving the request for analyzing the announcement content, the knowledge graph can be used for directly outputting an analysis result corresponding to the request for analyzing the announcement content. The embodiment generates the knowledge graph based on the chapter structure of the target announcement, and embeds the semantic knowledge of the chapter structure into the knowledge graph, so that the embodiment can improve the processing precision of the announcement and realize high-accuracy analysis of the announcement content.

The following describes the flows described in the above embodiments through embodiments in practical applications, and this embodiment provides a visual annotation labeling and training scheme for a chapter structure, and this embodiment constructs a visual labeling and training system for domain knowledge and an advertisement chapter structure, and this system can pre-calculate features of different granularities, such as a bulletin chapter, semantics, and the like, perform model training, prediction, and result correction for an advertisement mining flow, and implement incremental training to meet the processing requirements of complex texts. The present embodiment may include the following steps:

step 1: on the basis of the chapter structure, each element is labeled, and labeled tags can customize directories (layers), paragraphs, tables, other customized tags and the like, so that the chapter structure of the target bulletin is finally obtained.

Step 2: the elements are preprocessed to obtain the calculation results of various characteristics such as the entity, the relation, the key word, tfidf, the position information (the position of a relative title and a table), the text semantic vector, the similarity with each grade of title and the like of each element (a catalogue, a paragraph and a table) so as to construct the characteristic engineering of various elements in the chapter structure. These feature projects are stored in units of elements and can be invoked in the following rules and scripts in the form of objects and attributes.

The entities of the above elements may include company name, person name, time, location, product, event, attribute, industry, etc., the relationship may include a relationship between time, attribute and attribute value, tfidf refers to tfidf of the keyword, and the position information is a coordinate of the paragraph in the document. Feature engineering refers to the result of preprocessing, which functions to provide input for later invocation or machine learning. In particular, the invocation of feature engineering may be implemented by a reflection mechanism in a programming language.

And step 3: and (3) positioning, extracting, classifying and calculating the process of the mining task for constructing the notice, constructing a visual model training mode based on a neural network on the basis of the characteristic engineering, and introducing the characteristics of the context element of each element besides the own characteristics of the element when each element participates in training to realize characteristic enhancement.

Specifically, the implementation process of the step is as follows: constructing an announcement content analysis model of the target announcement; the system comprises a bulletin content analysis model, a query analysis module and a query analysis module, wherein the bulletin content analysis model comprises a positioning function module, an extraction function module, a classification function module and a calculation function module, the positioning function module is used for positioning query content, the extraction function module is used for extracting information corresponding to query conditions, the classification function module is used for outputting classification results corresponding to query problems, and the calculation function module is used for calculating calculation results corresponding to the query problems; inputting the feature engineering of each element and adjacent elements into the announcement content analysis model, training the announcement content analysis model to obtain a model processing result, and generating the indication map according to the model processing result. The classification function module can directly output results according to the query problem, and the calculation function module can execute at least one step of calculation operation according to the query problem and then output the results.

After the bulletin content analysis model is trained, the correction results of the positioning results, the extraction results, the classification results and the calculation results of the user can be received, and the bulletin content analysis model is subjected to incremental training according to the correction results.

And 4, step 4: the four arithmetic operations of constructing the visual regular expression facing the element can be used as one of the characteristics of the element, and the real-time calculation of one or more announcements can be realized. Uniformly accessing content elements and rules through an Object Relational Mapping (ORM), wherein the content elements can be called in a mode of Mapping objects in the rules to realize the expression of context characteristics; an interpreter is added before the rule engine executes to convert the context mapping object into a value so as to facilitate the execution. When the rule hits the text, the context and the current text can be highlighted through different colors, so that a developer can conveniently analyze and optimize the rule.

Specifically, the implementation process of the step is as follows: and constructing four operation rules of a regular expression according to the elements, performing rule matching on the feature engineering by using the four operation rules, and marking a text hitting the four operation rules so as to generate a knowledge graph of the target notice according to the rule matching result. The element-oriented in this step can be realized by "calling in the following rules and scripts in the form of objects and attributes" in step 2.

And 5: on the basis, the positioning, extracting, classifying and calculating processes are constructed into a visual pipeline process so as to realize a visual prediction result. And loading the data back to the corpus and the model after correcting the prediction result to realize incremental training, wherein the data generated in the process mainly comprises a chapter structure table (positions and indexes of a catalogue, a paragraph and a table), an element preprocessing characteristic table, a table content table, a text entity and relation table, an element labeling table and the prediction result.

Specifically, in the process of training the advertisement content analysis model, the training processes of the positioning function module, the extraction function module, the classification function module, and the calculation function module may be visualized, so as to display the positioning result of the positioning function module, the extraction result of the extraction function module, the classification result of the classification function module, and the calculation result of the calculation function module on a user interface.

Step 6: and constructing a domain ontology base according to the financial security domain knowledge and the chapter structure, loading the processing result into the knowledge graph, and calculating and judging the risks contained in the bulletins through the knowledge graph and the graph.

The ontology library refers to columns in elements and corresponding feature projects, the processing result refers to specific elements in each bulletin and rows in the feature projects, and the object of graph calculation is the ontology library and the result.

The embodiment provides a multi-task processing scheme fusing different granularities such as discourse, literal, semantic and the like, and the efficiency of advertisement mining is improved through an advertisement integrated preprocessing, training and predicting method; the embodiment also provides a visual bulletin processing method and a visual bulletin processing system, and the scheme precipitates a knowledge system for bulletin processing and reduces the development workload.

Referring to fig. 2, fig. 2 is a schematic structural diagram of a system for analyzing advertisement content according to an embodiment of the present application;

the system may include:

the annotation module 201 is configured to annotate elements in the target advertisement to obtain a chapter structure of the target advertisement;

the feature extraction module 202 is configured to extract element features of the target advertisement according to the discourse structure to obtain a feature engineering of each element;

a knowledge graph generation module 203, configured to generate a knowledge graph of the target advertisement according to the feature engineering;

the advertisement analysis module 204 is configured to, if an advertisement content analysis request is received, output an analysis result corresponding to the advertisement content analysis request by using the knowledge graph.

In the embodiment, the discourse structure is obtained by labeling the elements in the target bulletin, and the feature engineering of each element in the target bulletin is further extracted based on the discourse structure. The feature engineering may include feature information of each element, and the present embodiment may generate a knowledge graph of the target advertisement by using the feature engineering, where the knowledge graph includes all knowledge information in the target advertisement. After receiving the request for analyzing the announcement content, the knowledge graph can be used for directly outputting an analysis result corresponding to the request for analyzing the announcement content. The embodiment generates the knowledge graph based on the chapter structure of the target announcement, and embeds the semantic knowledge of the chapter structure into the knowledge graph, so that the embodiment can improve the processing precision of the announcement and realize high-accuracy analysis of the announcement content.

Further, the labeling module 201 is configured to label the directory, the paragraph, the table, and the user-defined element in the target advertisement to obtain a chapter structure of the target advertisement.

Further, the feature extraction module 202 is configured to pre-process the target advertisement according to the discourse structure to obtain an element feature; the element features comprise any one item or any combination of several items in element entities, entity relations, keywords, TF-IDF values of the keywords, paragraph position information, text semantic vectors of the paragraphs, and the similarity of the paragraphs and the titles at each level; and the system is further used for storing the element characteristics according to the corresponding relation between the element characteristics and the elements to obtain a characteristic project corresponding to each element.

Further, the knowledge-graph generating module 203 comprises:

the model training unit is used for constructing an announcement content analysis model of the target announcement; the system comprises a bulletin content analysis model, a query analysis module and a query analysis module, wherein the bulletin content analysis model comprises a positioning function module, an extraction function module, a classification function module and a calculation function module, the positioning function module is used for positioning query content, the extraction function module is used for extracting information corresponding to query conditions, the classification function module is used for outputting classification results corresponding to query problems, and the calculation function module is used for calculating calculation results corresponding to the query problems; the system is also used for inputting the characteristic engineering of each element and adjacent elements into the bulletin content analysis model, and training the bulletin content analysis model to obtain a model processing result; and the instruction map is generated according to the model processing result.

Further, the method also comprises the following steps:

and the visualization unit is used for performing visualization processing on the training processes of the positioning function module, the extraction function module, the classification function module and the calculation function module in the process of training the bulletin content analysis model so as to display the positioning result of the positioning function module, the extraction result of the extraction function module, the classification result of the classification function module and the calculation result of the calculation function module on a user interface.

Further, the method also comprises the following steps:

and the incremental training unit is used for receiving the correction results of the positioning result, the extraction result, the classification result and the calculation result of the user after the bulletin content analysis model is trained, and carrying out incremental training on the bulletin content analysis model according to the correction results.

The rule matching unit is used for constructing four operation rules of a regular expression according to the elements, performing rule matching on the feature engineering by using the four operation rules, and marking a text hitting the four operation rules; and the system is also used for generating a knowledge graph of the target notice according to the rule matching result.

Since the embodiment of the system part corresponds to the embodiment of the method part, the embodiment of the system part is described with reference to the embodiment of the method part, and is not repeated here.

The present application also provides a storage medium having a computer program stored thereon, which when executed, may implement the steps provided by the above-described embodiments. The storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The application further provides an electronic device, which may include a memory and a processor, where the memory stores a computer program, and the processor may implement the steps provided by the foregoing embodiments when calling the computer program in the memory. Of course, the electronic device may also include various network interfaces, power supplies, and the like.

The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.

It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.