CA3166094A1 - Commodity short title generation method and apparatus - Google Patents
Commodity short title generation method and apparatus Download PDFInfo
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- CA3166094A1 CA3166094A1 CA3166094A CA3166094A CA3166094A1 CA 3166094 A1 CA3166094 A1 CA 3166094A1 CA 3166094 A CA3166094 A CA 3166094A CA 3166094 A CA3166094 A CA 3166094A CA 3166094 A1 CA3166094 A1 CA 3166094A1
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- 238000000034 method Methods 0.000 title claims abstract description 108
- 230000011218 segmentation Effects 0.000 claims abstract description 89
- 230000009193 crawling Effects 0.000 claims abstract description 74
- 239000003607 modifier Substances 0.000 claims description 181
- 238000007873 sieving Methods 0.000 claims description 41
- 238000012545 processing Methods 0.000 claims description 25
- 238000013136 deep learning model Methods 0.000 claims description 19
- 238000001914 filtration Methods 0.000 claims description 17
- 238000012795 verification Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 7
- 238000002372 labelling Methods 0.000 abstract 1
- 230000009286 beneficial effect Effects 0.000 description 5
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/34—Browsing; Visualisation therefor
- G06F16/345—Summarisation for human users
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Control And Other Processes For Unpacking Of Materials (AREA)
- Bakery Products And Manufacturing Methods Therefor (AREA)
- Confectionery (AREA)
- Document Processing Apparatus (AREA)
Abstract
Description
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the technical field of text abstracting, and more particularly to a method and an apparatus for generating merchandise short-titles.
Description of Related Art
The purpose of short-titles is to describe key information of merchandise items with the least possible words so that users can get such key information at a glance.
An example of a short-title is "Korean-cutting all-over print dress." This can be regarded as a special text abstracting technology in the sense of natural language processing.
SUMMARY OF THE INVENTION
Date Regue/Date Received 2022-06-27
and
The original merchandise title data are segmented to obtain plural title words. These title words are entered into the word library to be matched with the key words. From the key words that have matches, at least two effective key words are sieved out, and stitched into Date Regue/Date Received 2022-06-27 a merchandise short-title according to the order of their parts of speech.
BRIEF DESCRIPTION OF THE DRAWINGS
DETAILED DESCRIPTION OF THE INVENTION
The word library is so established. Afterward, original merchandise title data are acquired and to be compressed. The original merchandise title data are segmented to obtain plural title words. These title words are entered into the word library to be matched with the key words. From the key words that have matches, at least two effective key words are sieved out, and stitched into a merchandise short-title according to the order of their parts of speech.
When the number of the left key words and the number of the key words in the word library are smaller than a threshold, it indicates that the key words in the word library are not robust enough. At this time, the key words in the merchandise title data that do not have matches are supplemented into the corresponding key word sets. The newly added key words are tagged by their parts of speech. On the contrary, if the number of the left key words and the number of the key words in the word library are greater than the threshold, it indicates that the collection of the key words in the word library is competent to deal with the Date Regue/Date Received 2022-06-27 current merchandise title data. Thus, a user can continue to crawl new merchandise title data and repeat the foregoing process to continuously assess the word library.
Exemplarily, the threshold is 3.
By using such a deep learning model to extract the key words that are modifier words or category words from the newly crawled merchandise title data, tagging the key words and adding them into the corresponding key word sets, the deep learning model demonstrates great adaptivity and can automatically recognizing category words and modifiers in the merchandise title according to contextual information.
Date Regue/Date Received 2022-06-27
The original merchandise title data are segmented into plural title words.
Then each of the title words is matched with the key words in the corresponding key word set, and the key words have matches in the original merchandise title data are sieved out.
if in the key words tagged as the modifier words, there are plural said key words in which the lexical scope of one said key word contains the lexical scope of another said key word, only the key word has the largest lexical scope is kept; if the key words tagged as the category words have word sense containing word sense of any said key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
and defining the left key words as the effective key words, and stitching them into the merchandise short-title according to locational sequence thereof. In practical implementations, the key words tagged as the category words in the original merchandise title data are processed first.
The storage medium may be a ROM/RAM, a hard drive, an optical disk, a memory card or the like.
Date Regue/Date Received 2022-06-27
Claims (310)
a data collecting unit, configured to crawl merchandise title data and/or collect search term data, to construct a corpus data set;
a word library unit, configured to:
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library;
a word tagging unit, configured to tag each key word in the word library as a modifier word or a category word according to a part of speech of word;
a word matching unit, configured to:
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
a processing unit, configured to:
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
DateRegue/DateReceived 2022-06-27 wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
DateRegue/DateReceived 2022-06-27 wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
deep learning model.
a data collecting unit, configured to crawl merchandise title data and/or collect search term data, to construct a corpus data set;
a word library unit, configured to:
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library;
DateRegue/DateReceived 2022-06-27 a word tagging unit, configured to tag each key word in the word library as a modifier word or a category word according to a part of speech of word;
a word matching unit, configured to:
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
a processing unit, configured to:
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
DateRegue/DateReceived 2022-06-27 extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
DateRegue/DateReceived 2022-06-27
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and DateRegue/DateReceived 2022-06-27 stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
deep learning model.
DateRegue/DateReceived 2022-06-27
crawling merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorizing corpuses in the corpus data set by merchandise categories;
extracting key words to construct a word library;
tagging each key word in the word library as a modifier word or a category word according to a part of speech of the key word;
performing word segmentation on original merchandise title data to obtain plural title words;
matching each of the title words with the key words in the word library;
outputting the key words with matches;
sieving out at least two effective key words from plural key words; and stitching the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
DateRegue/DateReceived 2022-06-27 wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
DateRegue/DateReceived 2022-06-27 recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
deep learning model.
a processor configured to:
crawl merchandise title data and/or collecting search term data, to construct a corpus data set;
DateRegue/DateReceived 2022-06-27 based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library;
tag each key word in the word library as a modifier word or a category word according to a part of speech of the key word;
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
DateRegue/DateReceived 2022-06-27 extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
DateRegue/DateReceived 2022-06-27
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and DateRegue/DateReceived 2022-06-27 stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
crawl merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library;
tag each key word in the word library as a modifier word or a category word according to a part of speech of the key word;
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
DateRegue/DateReceived 2022-06-27
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
DateRegue/DateReceived 2022-06-27 wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
DateRegue/DateReceived 2022-06-27 recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
deep learning model.
a data collecting unit, configured to crawl merchandise title data and/or collect search term data, to construct a corpus data set;
a word library unit, configured to:
DateRegue/DateReceived 2022-06-27 based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library; and a word tagging unit, configured to tag each key word in the word library as a modifier word or a category word according to a part of speech of word.
a word matching unit, configured to:
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
a processing unit, configured to:
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
DateRegue/DateReceived 2022-06-27 de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
DateRegue/DateReceived 2022-06-27 based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
DateRegue/DateReceived 2022-06-27 wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
a data collecting unit, configured to crawl merchandise title data and/or collect search term data, to construct a corpus data set;
a word library unit, configured to:
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library; and a word tagging unit, configured to tag each key word in the word library as a modifier word or a category word according to a part of speech of word.
a word matching unit, configured to:
DateRegue/DateReceived 2022-06-27 perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
a processing unit, configured to:
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
DateRegue/DateReceived 2022-06-27 extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
DateRegue/DateReceived 2022-06-27 segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
deep learning model.
DateRegue/DateReceived 2022-06-27 crawling merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorizing corpuses in the corpus data set by merchandise categories;
extracting key words to construct a word library; and tagging each key word in the word library as a modifier word or a category word according to a part of speech of the key word.
performing word segmentation on original merchandise title data to obtain plural title words;
matching each of the title words with the key words in the word library;
outputting the key words with matches;
sieving out at least two effective key words from plural key words; and stitching the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and DateRegue/DateReceived 2022-06-27 uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
DateRegue/DateReceived 2022-06-27 based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
DateRegue/DateReceived 2022-06-27 wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
a processor configured to:
crawl merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library; and tag each key word in the word library as a modifier word or a category word according to a part of speech of the key word.
the processor configured to:
DateRegue/DateReceived 2022-06-27 perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
DateRegue/DateReceived 2022-06-27 extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
DateRegue/DateReceived 2022-06-27 segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
DateRegue/DateReceived 2022-06-27
crawl merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract key words to construct a word library; and tag each key word in the word library as a modifier word or a category word according to a part of speech of the key word.
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with the key words in the word library;
output the key words with matches;
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
DateRegue/DateReceived 2022-06-27 performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
DateRegue/DateReceived 2022-06-27 based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
DateRegue/DateReceived 2022-06-27 wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
a word matching unit, configured to:
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with key words in a word library;
output the key words with matches;
a processing unit, configured to:
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
DateRegue/DateReceived 2022-06-27
a data collecting unit, configured to crawl merchandise title data and/or collect search term data, to construct a corpus data set;
a word library unit, configured to:
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract the key words to construct the word library; and a word tagging unit, configured to tag each key word in the word library as a modifier word or a category word according to a part of speech of word.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
DateRegue/DateReceived 2022-06-27
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
DateRegue/DateReceived 2022-06-27 matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and DateRegue/DateReceived 2022-06-27 outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
a word matching unit, configured to:
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with key words in a word library;
output the key words with matches;
a processing unit, configured to:
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
a data collecting unit, configured to crawl merchandise title data and/or collect search term data, to construct a corpus data set;
a word library unit, configured to:
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract the key words to construct the word library; and a word tagging unit, configured to tag each key word in the word library as a modifier word or a category word according to a part of speech of word.
DateRegue/DateReceived 2022-06-27
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
DateRegue/DateReceived 2022-06-27 wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
DateRegue/DateReceived 2022-06-27 recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
deep learning model.
performing word segmentation on original merchandise title data to obtain plural title words;
matching each of the title words with key words in a word library;
outputting the key words with matches;
sieving out at least two effective key words from plural key words; and DateRegue/DateReceived 2022-06-27 stitching the effective key words into merchandise short-title according to their parts of speech.
crawling merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorizing corpuses in the corpus data set by merchandise categories;
extracting the key words to construct the word library; and tagging each key word in the word library as a modifier word or a category word according to a part of speech of the key word.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
DateRegue/DateReceived 2022-06-27
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
DateRegue/DateReceived 2022-06-27 matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and DateRegue/DateReceived 2022-06-27 outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
a processor configured to:
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with key words in a word library;
output the key words with matches;
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
the processor configured to:
crawl merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract the key words to construct the word library; and tag each key word in the word library as a modifier word or a category word according to a part of speech of the key word.
DateRegue/DateReceived 2022-06-27
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
DateRegue/DateReceived 2022-06-27 wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
DateRegue/DateReceived 2022-06-27 recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
perform word segmentation on original merchandise title data to obtain plural title words;
match each of the title words with key words in a word library;
DateRegue/DateReceived 2022-06-27 output the key words with matches;
sieve out at least two effective key words from plural key words; and stitch the effective key words into merchandise short-title according to their parts of speech.
crawl merchandise title data and/or collecting search term data, to construct a corpus data set;
based on a merchandise category table, categorize corpuses in the corpus data set by merchandise categories;
extract the key words to construct the word library; and tag each key word in the word library as a modifier word or a category word according to a part of speech of the key word.
based on the merchandise category table, categorizing the corpuses in the corpus data set one by one according to the merchandise categories;
performing word segmentation on the corpuses, to obtain the plural key words;
de-duplicating and filtering the key words in every merchandise category to obtain key word sets each corresponding to the merchandise category; and uniting the key words sets to form the word library.
DateRegue/DateReceived 2022-06-27 extracting the key words which are the modifier words or the category words from the word library by means of manual tagging and tagging corresponding parts of speech;
extracting the key words that are the modifier words or the category words from the word library using a machine tagging model and tagging corresponding parts of speech.
crawling new merchandise title data;
performing word segmentation on the new merchandise title data;
matching resulting words with the key words in the word library;
wherein a number of the key words have matches is smaller than a threshold, adding the key words in the new merchandise title data into corresponding key word sets, and tagging newly added key words for their parts of speech;
wherein the number of the key words have matches is greater than the threshold, crawling the new merchandise title data, performing word segmentation on the new merchandise title data, and matching resulting words with the key words in the word library.
based on a semantic recognition technology in a machine model, extracting the key words that are the modifier words or the category words from newly crawled merchandise title data, adding them into the corresponding key word sets, and tagging the newly added key words for their corresponding parts of speech.
DateRegue/DateReceived 2022-06-27
recognizing the merchandise categories in the original merchandise title data;
matching the merchandise categories with the key word sets;
segmenting the original merchandise title data into the plural title words;
matching each of the title words with the key words in the key word set; and sieving out the key words with matches.
recording location information of each of the key words in the original merchandise title data;
wherein the key words tagged as the modifier words, there are plural key words whose lexical scopes have intersection, only one key word in the intersection is kept;
wherein the key words tagged as the modifier words, there are plural key words in which the lexical scope of one key word contains the lexical scope of another key word, only the key word has largest lexical scope is kept;
wherein the key words tagged as the category words have word sense containing word sense of any key word tagged as the modifier word, the key word corresponding to the modifier word is removed;
defining remaining key words as the effective key words; and DateRegue/DateReceived 2022-06-27 stitching the remaining key words into the merchandise short-title according to locational sequence.
matching different original merchandise title data with the word library;
performing parallel processing; and outputting plural corresponding merchandise short-titles.
DateRegue/DateReceived 2022-06-27
DateRegue/DateReceived 2022-06-27
Priority Applications (2)
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CA3217669A CA3217669A1 (en) | 2019-12-27 | 2020-08-28 | Commodity short title generation method and apparatus |
CA3217721A CA3217721A1 (en) | 2019-12-27 | 2020-08-28 | Commodity short title generation method and apparatus |
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CN201911373120.5 | 2019-12-27 | ||
CN201911373120.5A CN111191022B (en) | 2019-12-27 | 2019-12-27 | Commodity short header generation method and device |
PCT/CN2020/111943 WO2021128914A1 (en) | 2019-12-27 | 2020-08-28 | Commodity short title generation method and apparatus |
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CA3166094A Pending CA3166094A1 (en) | 2019-12-27 | 2020-08-28 | Commodity short title generation method and apparatus |
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CN111191022B (en) * | 2019-12-27 | 2023-07-25 | 苏宁云计算有限公司 | Commodity short header generation method and device |
CN112446208A (en) * | 2020-12-09 | 2021-03-05 | 北京有竹居网络技术有限公司 | Method, device and equipment for generating advertisement title and storage medium |
CN112579776A (en) * | 2020-12-21 | 2021-03-30 | 北京智齿博创科技有限公司 | Automatic labeling method of quality problem scene labels based on categories |
CN113821718A (en) * | 2021-02-01 | 2021-12-21 | 北京沃东天骏信息技术有限公司 | Article information pushing method and device |
CN113343687B (en) * | 2021-05-25 | 2023-09-05 | 北京奇艺世纪科技有限公司 | Event name determining method, device, equipment and storage medium |
CN113283218A (en) * | 2021-06-24 | 2021-08-20 | 中国平安人寿保险股份有限公司 | Semantic text compression method and computer equipment |
CN113553838A (en) * | 2021-08-03 | 2021-10-26 | 稿定(厦门)科技有限公司 | Commodity file generation method and device |
CN115169337B (en) * | 2022-08-24 | 2023-02-14 | 中教畅享(北京)科技有限公司 | Method for calculating keyword score in commodity title optimization |
CN115470322B (en) * | 2022-10-21 | 2023-05-05 | 深圳市快云科技有限公司 | Keyword generation system and method based on artificial intelligence |
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US8489609B1 (en) * | 2006-08-08 | 2013-07-16 | CastTV Inc. | Indexing multimedia web content |
CN102012915A (en) * | 2010-11-22 | 2011-04-13 | 百度在线网络技术(北京)有限公司 | Keyword recommendation method and system for document sharing platform |
CN104424296B (en) * | 2013-09-02 | 2018-07-31 | 阿里巴巴集团控股有限公司 | Query word sorting technique and device |
CN104636334A (en) * | 2013-11-06 | 2015-05-20 | 阿里巴巴集团控股有限公司 | Keyword recommending method and device |
CN106708813A (en) * | 2015-07-14 | 2017-05-24 | 阿里巴巴集团控股有限公司 | Title processing method and equipment |
KR20180069813A (en) * | 2015-10-16 | 2018-06-25 | 알리바바 그룹 홀딩 리미티드 | Title display method and apparatus |
CN108804541B (en) * | 2018-05-08 | 2020-09-18 | 苏州闻道网络科技股份有限公司 | Electric trademark optimization system and optimization method |
CN111191022B (en) * | 2019-12-27 | 2023-07-25 | 苏宁云计算有限公司 | Commodity short header generation method and device |
-
2019
- 2019-12-27 CN CN201911373120.5A patent/CN111191022B/en active Active
-
2020
- 2020-08-28 CA CA3217721A patent/CA3217721A1/en active Pending
- 2020-08-28 WO PCT/CN2020/111943 patent/WO2021128914A1/en active Application Filing
- 2020-08-28 CA CA3217669A patent/CA3217669A1/en active Pending
- 2020-08-28 CA CA3166094A patent/CA3166094A1/en active Pending
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WO2021128914A1 (en) | 2021-07-01 |
CA3217669A1 (en) | 2021-07-01 |
CN111191022A (en) | 2020-05-22 |
CN111191022B (en) | 2023-07-25 |
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