CN113886588A - Major professional employment direction identification method based on recruitment text mining - Google Patents
Major professional employment direction identification method based on recruitment text mining Download PDFInfo
- Publication number
- CN113886588A CN113886588A CN202111220573.1A CN202111220573A CN113886588A CN 113886588 A CN113886588 A CN 113886588A CN 202111220573 A CN202111220573 A CN 202111220573A CN 113886588 A CN113886588 A CN 113886588A
- Authority
- CN
- China
- Prior art keywords
- recruitment
- word
- data
- professional
- employment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000007115 recruitment Effects 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000005065 mining Methods 0.000 title claims abstract description 19
- 238000003064 k means clustering Methods 0.000 claims abstract description 15
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 239000013598 vector Substances 0.000 claims description 17
- 230000011218 segmentation Effects 0.000 claims description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000011160 research Methods 0.000 claims description 4
- 230000009193 crawling Effects 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 claims description 2
- 238000012216 screening Methods 0.000 claims description 2
- 238000004088 simulation Methods 0.000 claims description 2
- 238000012549 training Methods 0.000 claims description 2
- 238000013480 data collection Methods 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000012258 culturing Methods 0.000 description 1
- 230000009849 deactivation Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/353—Clustering; Classification into predefined classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/216—Parsing using statistical methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/237—Lexical tools
- G06F40/242—Dictionaries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Probability & Statistics with Applications (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a major professional employment direction recognition method based on recruitment text mining, which takes recruitment information of a recruitment website as a data source and analyzes a major recruitment position name through 4 steps of main data acquisition, data preprocessing, word vectorization and K-means clustering to obtain a major professional employment direction. The method for cultivating and researching the professional directional talents based on text mining can quickly, efficiently and accurately identify the employment direction requirements of employment markets on the professional talents from the network recruitment text data, can optimize and improve professional talent cultivation schemes for colleges and universities, and provides decision support for cultivating the professional directional talents meeting the market requirements.
Description
Technical Field
The invention relates to the field of major employment direction recognition, in particular to a major employment direction recognition method based on recruitment text mining.
Background
Along with diversification of professional education in colleges and universities, employment directions of professional talents are wider, and requirements of enterprises on talents in different directions of the profession are different. Therefore, under the background that the contradiction between supply and demand of talents is increasingly prominent, the accurate insight of the employment direction demand of the market for the professional talents is the key for the colleges and universities to cultivate the professional talents meeting the market demand, promote the employment of the professional talents and solve the contradiction between supply and demand of talents.
According to a '2020 Chinese network recruitment industry market development research report' issued by the ai rui network, the number of enterprise employers in 2019 network recruitment reaches 486.6 thousands, and the network recruitment becomes a main enterprise recruitment mode, and the extraction of enterprise recruitment requirements from network recruitment information is an effective path for acquiring employment market requirements. Text mining is a technique that can extract meaningful information from unstructured text data.
Disclosure of Invention
The invention aims to provide a major professional employment direction identification method based on recruitment text mining, which acquires major recruitment post name data from a recruitment website through a text mining technology, analyzes major professional employment directions, optimizes and improves a major talent culture scheme for colleges and universities, and provides decision support for culturing a major direction talent meeting market requirements.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a major professional employment direction recognition method based on recruitment text mining comprises the following steps:
step 1: data acquisition, namely crawling a professional recruitment post name from a selected recruitment website by using a Web crawler technology by taking the professional name as a keyword;
step 2: preprocessing data, namely preprocessing the collected recruitment post name data;
and step 3: performing Word vectorization, namely performing Word vectorization on the recruitment position name by adopting a Word2vec algorithm to obtain vector representation of each recruitment position name;
and 4, step 4: and (4) carrying out K-means clustering, and carrying out clustering analysis on the recruitment post names by using a K-means clustering algorithm to obtain the major employment direction of the specialty.
Preferably, the data acquisition comprises the following sub-steps:
step 1.1: formulating a crawler rule, and determining a webpage URL, a page number range and a post screening condition for acquiring the recruitment post name data;
step 1.2: and the Web crawler acquires the professional network recruitment post name by adopting a Web crawler technology according to the formulated crawler rule.
Preferably, the data preprocessing comprises the following sub-steps:
step 2.1: data cleaning, namely cleaning the collected recruitment post name data to remove data noise in the data, wherein the data noise comprises a null value, a repetition value, an abnormal value, an HTML (hypertext markup language) label and the like;
step 2.2: constructing a custom dictionary, selecting a special post name combination word from the data after word segmentation and stop word removal processing, putting the combination word into the custom word segmentation dictionary, and selecting a word without research meaning to put into a custom stop word library;
step 2.3: and segmenting words and stop words, segmenting data by using a Jieba segmentation program package in Python and a constructed custom segmentation dictionary, and selecting a Hardset stop word list and combining the constructed custom segmentation dictionary to perform stop word processing.
Preferably, said word vectorization comprises the sub-steps of:
step 3.1: and initializing word vectors, and initializing dictionary vector representation by utilizing a uniformly distributed random fixed-length sequence initialization dictionary.
Step 3.2: training word vectors, modeling a problem into the context of a given target word through a conditional probability model, and predicting a language model of the target word; the vector expression of the target word is obtained by utilizing the gradient descent and the back propagation to maximize the log-likelihood target function,
in the formula, P (omega)t|ωt-c：ωt+c) Is the conditional probability, T is the length of the sentence; omegatFor predicted target words, c is context size; omegat-c：ωt+cThe target word does not contain the first c to the last c words of the target word;
the conditional probability P is obtained by softmax,
in the formula, N is the size of a word list;
is composed of
Transposing;
is a vector representation of the target word; exp () is an exponential function with a natural constant as the base; v. ofnIs the vector representation of the nth time in the word list; v. ofjIs a vector representation of the jth word in the context of the target word.
Preferably, the K-means clustering comprises the following sub-steps:
step 4.1: performing K-means clustering on the recruitment position names, performing clustering analysis on the recruitment position names by using a K-means clustering algorithm, wherein the K-means algorithm takes the minimum value of the square error sum SSE of the sample and the mass center as an objective function, and the calculation formula is as follows:
in the formula: k is the number of clusters, EiIs the ith cluster; e.g. of the typeiIs EiThe center of mass of; x is EiA knowledge point sample of (1); n is a radical ofiIs EiThe number of samples in (1);
selecting the minimum K meeting the following constraint formula as the optimal clustering number, namely the value of the K,
Gapk≥Gapk+1-sk+1 (6)
b is simulation times of Monte Carlo simulation calculation; SSEkObtaining SSE obtained by calculation when the K value is K for the current sample; SSEkbTaking the K value as K, and carrying out the quadratic error sum of the mass centers when carrying out the b-th Monte Carlo simulation calculation;
step 4.2: and (5) summarizing the main employment direction, and summarizing each post after the K-means clustering to obtain the professional main employment direction.
The invention has the following effective benefits: compared with traditional investigation methods such as questionnaire investigation, enterprise visit, expert consultation and the like, the employment direction requirements of employment markets for professional talents can be quickly, efficiently and accurately identified from the network recruitment text data by adopting the text mining technology. The method adopts a text mining technology to deeply mine the professional network recruitment post names, obtains the professional main employment direction through 4 steps of main data acquisition, data preprocessing, word vectorization and K-means clustering, and provides decision support for optimization and improvement of talent culture schemes in colleges and universities.
Drawings
Fig. 1 is a major employment direction recognition method for major specialization based on recruitment text mining according to the present invention.
FIG. 2 is a flow of custom thesaurus construction.
FIG. 3 is a graph of Gap value as a function of k value.
Detailed Description
In order to make the technical features, objects and effects of the present invention more clearly understood, the present invention will be further described in detail with reference to the accompanying drawings and examples. The embodiments described herein are only for explaining the technical solution of the present invention and are not limited to the present invention.
The invention provides a major professional employment direction identification method based on recruitment text mining, which has the process shown in figure 1 and takes industrial engineering major as an example, and the implementation comprises the following steps:
step 1: data acquisition, namely selecting the current popular recruitment website with no worry (https:// www.51job.com) by adopting a web crawler technology, and crawling the recruitment post name data of industrial engineering major nationwide by taking industrial engineering as a search keyword;
step 2: data preprocessing, namely performing data cleaning operations such as duplicate removal, null removal and noise removal on the collected post name data, constructing a customized word segmentation dictionary and a disabled word library according to a customized word library construction process shown in fig. 2, and performing word segmentation and word deactivation processing on recruitment data by combining a Jieba word segmentation program package and a Haugh disabled word list in Python, wherein 8169 effective industrial engineering professional recruitment post name data are obtained before and after post name preprocessing as shown in table 1.
TABLE 1 post name data comparison before and after preprocessing
And step 3: and (4) performing Word vectorization, namely performing Word vectorization on the recruitment position name data through a Word2vec algorithm to obtain vector expression of each position name.
And 4, step 4: and (3) performing K-means clustering, namely clustering the names of the recruitment posts by adopting a K-means algorithm, wherein the change curve of gap (K) is shown in figure 3, so that the optimal clustering number K is 4. The clustering results of the position names are shown in table 2, and analyzing the position names in each category can find out that: an industrial engineer, a process engineer, an IE engineer and the like in the category 1 can belong to an engineering post, a production plan, lean production, production management and the like in the category 2 can belong to a production post, a logistics specialist, a supply chain director, a supply chain specialist and the like in the category 3 can belong to a logistics supply chain post, and an ERP implementation consultant, an SAP implementation consultant and an MES implementation consultant in the category 4 can belong to a consultation consultant post. Through the cluster analysis of the post names, 4 main employment directions of industrial engineering major, such as engineering management, production management, logistics supply chain and consultants can be obtained.
TABLE 2 post name clustering results
Through example research, the major professional employment direction identification method based on recruitment text mining, provided by the invention, can quickly, efficiently and accurately identify the employment direction requirements of employment markets on professional talents from the network recruitment text data, can be applied and popularized in various professional fields, and provides decision support for colleges and universities to optimize and improve professional talent culture schemes and culture professional talents meeting the market requirements.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present invention shall be included in the protection scope of the claims of the present invention.
Claims (5)
1. A major professional employment direction recognition method based on recruitment text mining is characterized by comprising the following steps:
step 1: data acquisition, namely crawling a professional recruitment post name from a selected recruitment website by using a Web crawler technology by taking the professional name as a keyword;
step 2: preprocessing data, namely preprocessing the collected recruitment post name data;
and step 3: performing Word vectorization, namely performing Word vectorization on the recruitment position name by adopting a Word2vec algorithm to obtain vector representation of each recruitment position name;
and 4, step 4: and (4) carrying out K-means clustering, and carrying out clustering analysis on the recruitment post names by using a K-means clustering algorithm to obtain the major employment direction of the specialty.
2. The method for identifying major employment directions for professionals based on recruitment text mining as claimed in claim 1, wherein the data collection of the step 1 comprises the following substeps:
step 1.1: formulating a crawler rule, and determining a webpage URL, a page number range and a post screening condition for acquiring the recruitment post name data;
step 1.2: and the Web crawler acquires the professional network recruitment post name by adopting a Web crawler technology according to the formulated crawler rule.
3. The method for identifying major employment directions for professionals based on recruitment text mining as claimed in claim 1, wherein the data preprocessing of the step 2 comprises the following sub-steps:
step 2.1: data cleaning, namely cleaning the collected recruitment post name data to remove data noise in the data, wherein the data noise comprises a null value, a repetition value, an abnormal value and an HTML (hypertext markup language) label;
step 2.2: constructing a custom dictionary, selecting a special post name combination word from the data after word segmentation and stop word removal processing, putting the combination word into the custom word segmentation dictionary, and selecting a word without research meaning to put into a custom stop word library;
step 2.3: and segmenting words and stop words, segmenting data by using a Jieba segmentation program package in Python and a constructed custom segmentation dictionary, and selecting a Hardset stop word list and combining the constructed custom segmentation dictionary to perform stop word processing.
4. The professional major employment direction recognition method based on recruitment text mining as claimed in claim 1, wherein the word vectorization of step 3 comprises the following sub-steps:
step 3.1: and initializing word vectors, and initializing dictionary vector representation by utilizing a uniformly distributed random fixed-length sequence initialization dictionary.
Step 3.2: training word vectors, modeling a problem into the context of a given target word through a conditional probability model, and predicting a language model of the target word; the vector expression of the target word is obtained by utilizing the gradient descent and the back propagation to maximize the log-likelihood target function,
in the formula, P (omega)t|ωt-c：ωt+c) Is the conditional probability, T is the length of the sentence; omegatFor predicted target words, c is context size; omegat-c：ωt+cThe target word does not contain the first c to the last c words of the target word;
the conditional probability P is obtained by softmax,
in the formula, N is the size of a word list;
is composed of
Transposing;
is a vector representation of the target word; exp () is an exponential function with a natural constant as the base; v. ofnIs the vector representation of the nth time in the word list; v. ofjIs a vector representation of the jth word in the context of the target word.
5. The method for identifying major employment directions for expertise based on recruitment text mining as claimed in any one of claims 1 to 4, wherein the K-means clustering of step 4 comprises the following sub-steps:
step 4.1: performing K-means clustering on the recruitment position names, performing clustering analysis on the recruitment position names by using a K-means clustering algorithm, wherein the K-means algorithm takes the minimum value of the square error sum SSE of the sample and the mass center as an objective function, and the calculation formula is as follows:
in the formula: k is the number of clusters, EiIs the ith cluster; e.g. of the typeiIs EiThe center of mass of; x is EiA knowledge point sample of (1); n is a radical ofiIs EiThe number of samples in (1);
selecting the minimum K meeting the following constraint formula as the optimal clustering number, namely the value of the K,
Gapk≥Gapk+1-sk+1 (6)
b is simulation times of Monte Carlo simulation calculation; SSEkObtaining SSE obtained by calculation when the K value is K for the current sample; SSEkbTaking the K value as K, and carrying out the quadratic error sum of the mass centers when carrying out the b-th Monte Carlo simulation calculation;
step 4.2: and (5) summarizing the main employment direction, and summarizing each post after the K-means clustering to obtain the professional main employment direction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111220573.1A CN113886588A (en) | 2021-10-20 | 2021-10-20 | Major professional employment direction identification method based on recruitment text mining |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111220573.1A CN113886588A (en) | 2021-10-20 | 2021-10-20 | Major professional employment direction identification method based on recruitment text mining |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113886588A true CN113886588A (en) | 2022-01-04 |
Family
ID=79003645
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111220573.1A Pending CN113886588A (en) | 2021-10-20 | 2021-10-20 | Major professional employment direction identification method based on recruitment text mining |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113886588A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523225A (en) * | 2023-04-18 | 2023-08-01 | 泸州职业技术学院 | Data mining-based overturning classroom hybrid teaching method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110008338A (en) * | 2019-03-04 | 2019-07-12 | 华南理工大学 | A kind of electric business evaluation sentiment analysis method of fusion GAN and transfer learning |
| CN112861530A (en) * | 2021-03-17 | 2021-05-28 | 华南农业大学 | Course setting analysis method based on text mining |
| CN113139599A (en) * | 2021-04-22 | 2021-07-20 | 北方工业大学 | Service distributed clustering method fusing word vector expansion and topic model |
-
2021
- 2021-10-20 CN CN202111220573.1A patent/CN113886588A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110008338A (en) * | 2019-03-04 | 2019-07-12 | 华南理工大学 | A kind of electric business evaluation sentiment analysis method of fusion GAN and transfer learning |
| CN112861530A (en) * | 2021-03-17 | 2021-05-28 | 华南农业大学 | Course setting analysis method based on text mining |
| CN113139599A (en) * | 2021-04-22 | 2021-07-20 | 北方工业大学 | Service distributed clustering method fusing word vector expansion and topic model |
Non-Patent Citations (1)
| Title |
|---|
| 朱爱璐: "基于文本挖掘的数据分析岗位人才需求分析", 中国优秀硕士学位论文全文数据库（电子期刊）, no. 11, 15 November 2020 (2020-11-15), pages 123 - 81 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523225A (en) * | 2023-04-18 | 2023-08-01 | 泸州职业技术学院 | Data mining-based overturning classroom hybrid teaching method |
| CN116523225B (en) * | 2023-04-18 | 2024-01-23 | 泸州职业技术学院 | Data mining-based overturning classroom hybrid teaching method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mohan et al. | Stock price prediction using news sentiment analysis | |
| CN110825882A (en) | Knowledge graph-based information system management method | |
| US20180053107A1 (en) | Aspect-based sentiment analysis | |
| Mačutek et al. | Evaluating goodness-of-fit of discrete distribution models in quantitative linguistics | |
| US10726058B2 (en) | System, computer program product and method for generating embeddings of textual and quantitative data | |
| CN115293131B (en) | Data matching method, device, equipment and storage medium | |
| Wang et al. | A context-aware recommendation system for improving manufacturing process modeling | |
| Munna et al. | Sentiment analysis and product review classification in e-commerce platform | |
| CN113255321A (en) | Financial field chapter-level event extraction method based on article entity word dependency relationship | |
| CN111709225B (en) | Event causal relationship discriminating method, device and computer readable storage medium | |
| CN111651994B (en) | Information extraction method and device, electronic equipment and storage medium | |
| ul Hassan et al. | Multi-class categorization of design-build contract requirements using text mining and natural language processing techniques | |
| CN112861530A (en) | Course setting analysis method based on text mining | |
| CN113886588A (en) | Major professional employment direction identification method based on recruitment text mining | |
| Wang et al. | A Systematic Mapping Study of Information Retrieval Approaches Applied to Requirements Trace Recovery. | |
| CN117574858A (en) | Automatic generation method of class case retrieval report based on large language model | |
| Rahman et al. | Example driven code review explanation | |
| CN113806538B (en) | Label extraction model training method, device, equipment and storage medium | |
| CN115455198A (en) | Model training method, legal action information alignment and fusion method and terminal equipment thereof | |
| Mankolli et al. | A hybrid machine learning method for text analysis to determine job titles similarity | |
| Radomirović et al. | Text document clustering approach by improved sine cosine algorithm | |
| Pant et al. | Automatic Software Engineering Position Resume Screening using Natural Language Processing, Word Matching, Character Positioning, and Regex | |
| Trinh et al. | Automatic process resume in talent pool by applying natural language processing | |
| Poovizhi et al. | Automatic scraping of employment record using machine learning—An assistance for the recruiter | |
| CN109885647B (en) | User history verification method, device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |