WO2020111827A1 - Serveur et procédé de génération de profil automatique - Google Patents

Serveur et procédé de génération de profil automatique Download PDF

Info

Publication number
WO2020111827A1
WO2020111827A1 PCT/KR2019/016608 KR2019016608W WO2020111827A1 WO 2020111827 A1 WO2020111827 A1 WO 2020111827A1 KR 2019016608 W KR2019016608 W KR 2019016608W WO 2020111827 A1 WO2020111827 A1 WO 2020111827A1
Authority
WO
WIPO (PCT)
Prior art keywords
profile
information
keywords
keyword
profile information
Prior art date
Application number
PCT/KR2019/016608
Other languages
English (en)
Korean (ko)
Inventor
정희동
이상범
조민희
김동희
Original Assignee
주식회사 로켓펀치
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 주식회사 로켓펀치 filed Critical 주식회사 로켓펀치
Publication of WO2020111827A1 publication Critical patent/WO2020111827A1/fr

Links

Images

Classifications

    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • 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
    • G06F16/33—Querying
    • 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
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/3332—Query translation
    • G06F16/3334—Selection or weighting of terms from queries, including natural language queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/268—Morphological analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • Artificial intelligence technology is being researched and developed in various fields. Recently, artificial intelligence programs that can be useful in real life, such as big data analysis, voice recognition, and language implication recognition, have spread and are used in various smart terminals.
  • the language implication recognition field enables advanced data processing such as interpretation, classification, and inference of language contents included in documents to be performed by an automated system rather than a person.
  • the artificial intelligence technology related to language processing has recently been applied to smart device control and smart home service to make it more convenient to control the smart terminal.
  • Artificial intelligence related to language recognition can be used not only for speech recognition, but also for interpreting recorded language information such as documents, sentences, and word recognition and extracting important information.
  • Profile information managed manually can be automated by creating and updating profile information through language recognition technology.
  • the conventional language recognition technology mainly grasps the meaning of words through morpheme classification and analysis, profile information in which pronouns, foreign words, and new words are frequently used is often recognized incorrectly.
  • profile information that automatically collects various business information such as corporate information, personal information, and book information required by the economic population and is collected in data units, and automatically extracts and processes the automatically collected data in a form convenient for people to utilize Provide a generating server and method.
  • Profile automatic generation server includes a collection module that periodically collects articles including articles, columns, and interviews in a web space including a news site and a blog; A database for storing the collected document and the source and web space information of the document, and storing profile generation information including keywords for generating profile information from the document and tags indicating business information and information categories including keywords; An extraction module that analyzes sentences included in the document from which profile information is to be extracted, extracts keywords, and generates profile preliminary information by tagging tag information, which is profile category information, in each letter constituting the keyword; And a generation module for collecting the extracted profile preliminary information, merging successively tagged texts to generate keywords that are profile information, and classifying keywords and tags to generate profile information. It includes.
  • a method for automatically generating a profile includes (A) the collecting module periodically collecting a document including an article, a column, and an interview in a web space including a news site and a blog; (B) The database stores the collected document and the source and web space information of the document, and stores the profile generation information including keywords for generating profile information from the document and tags indicating business information and information categories containing the keyword. To do; (C) The extraction module analyzes sentences included in the document to extract profile information, extracts keywords, and generates profile preliminary information by tagging tag information, which is profile category information, in each letter constituting the keyword. To do; And (D) generating a module to collect the extracted profile preliminary information, to generate keywords that are profile information by merging successively tagged characters, and to generate profile information by classifying keywords and tags; It includes.
  • the profile information generation server and method according to the embodiment enable automatic and accurate extraction of profile information, which is important information about people, companies, and products from various online contents.
  • the accuracy and speed of profile information extraction can be improved.
  • the reliability of the keyword is calculated so that it is possible to grasp how accurate the specific profile information is.
  • the profile information generation server and method automatically prevents the generation of incorrect profile information and the spread of information by automatically calculating the reliability of the profile information, separating profile data of the same person, and continuously updating the profile information. .
  • FIG. 1 is a diagram showing an approximate data processing block of a profile creation server according to an embodiment.
  • FIG. 2 is a view showing in more detail the data processing block of the profile information generation server according to the embodiment.
  • 3 is a view for explaining the machine learning process of the profile information generation server according to the embodiment
  • FIG. 4 is a view for explaining a process of generating profile information according to an embodiment
  • FIG. 5 is a diagram showing a data processing flow for automatically generating profile information according to an embodiment
  • FIG. 6 is a diagram showing a data processing process for generating profile preliminary information according to an embodiment
  • FIG. 7 is a view for explaining a profile information generation process according to an embodiment
  • Profile automatic generation server includes a collection module that periodically collects articles including articles, columns, and interviews in a web space including a news site and a blog; A database that stores the collected document, the source and web space information of the document, and stores profile generation information including keywords for generating profile information from the document and tags indicating information categories including keywords and business information. ; Extraction module that analyzes sentences included in the document to extract profile information, extracts keywords, and generates profile preliminary information by tagging tag information, which is profile category information, in each letter constituting the keyword; And a generation module for collecting the extracted profile preliminary information, merging successively tagged texts to generate keywords that are profile information, and classifying the keywords and tags to generate profile information. It includes.
  • FIG. 1 is a diagram showing an approximate data processing block of a profile creation server according to an embodiment.
  • the profile generation server may include a collection module 110, a database 130, an extraction module 150, and a generation module 170.
  • the term'module' should be interpreted to include software, hardware, or a combination thereof, depending on the context in which the term is used.
  • the software may be machine language, firmware, embedded code, and application software.
  • the hardware can be a circuit, processor, computer, integrated circuit, integrated circuit core, sensor, micro-electro-mechanical system (MEMS), passive device, or combinations thereof.
  • MEMS micro-electro-mechanical system
  • the collection module 110 periodically collects documents from various web spaces and external servers. For example, the collection module 110 periodically collects document data in which articles, columns, interviews, and the like are recorded in web sites such as news sites, blogs, and various SNS.
  • the database 130 stores a series of data necessary for generating profile information, such as the collected document and the source of the document and web space information and profile creation information.
  • keywords, tags, and the like necessary for generating profile information may be stored in the database 130.
  • keywords are content data representing profile information as words and proper nouns extracted from sentences input to the server.
  • the tag is a category of keyword and profile information, and may be higher information of a specific keyword. For example, when the keyword is'manager', the tag of the'manager' keyword may be'position', and when the keyword is '30', the tag of the '30' keyword may be'age'.
  • the database 130 accumulates and stores keywords and tags and profile information generated by keywords and tags, and updates and stores changed profile information of the same person.
  • the extraction module 150 analyzes sentences included in the document from which profile information is to be extracted, and extracts keywords from the sentences. Subsequently, tag preliminary information is generated by tagging the tags constituting the keyword with tags indicating the profile category information.
  • tag preliminary information is generated by tagging the tags constituting the keyword with tags indicating the profile category information.
  • the representative manager of Elvision, Inc. is a veteran with over 10 years of industry experience' is entered as a server, extracting'Elvision' as a keyword in each letter constituting'Elvision' Add tags.
  • data such as'L_company, non-company, all_company' may be profile preliminary information.
  • the tag information added to the keyword may be selected through other keywords adjacent to the specific keyword, or may be used by loading accumulated keyword tag information in the database.
  • Elvision can recognize the word adjacent to another keyword, Inc., and select tag information added to each word constituting the keyword Elvision as a'company'.
  • the generation module 170 collects the extracted profile preliminary information to generate keywords, and classifies the keywords according to the profile information category. For example, when the same tag is continuously added to each letter, the generation module 170 merges the letters having the same tag to generate a keyword. Specifically, when the company tags appear consecutively, the words'L','B', and'I' tagged with the same tag are respectively collected and merged to generate the keyword'LVI'. Subsequently, the generation module 170 generates and displays profile information classified by sorting keywords according to tag information tagged to the keyword. Continuing the above example, it is possible to generate profile information that classifies keywords and tag information assigned to keywords in the form of'Company: Elvision'.
  • the generation module 170 stores the keyword after generating it, and in the process of merging the tagged words when analyzing new input data, if the merged word is equal to or more than a predetermined percentage, the previously stored keyword is recommended. can do.
  • the generation module 170 continues the letters'L_company, non-company'.
  • the generation module 170 calculates the match rate of the letters and tags constituting the previously stored keyword'Elvision', and when the calculated match rate is above a certain level (reference value),'Elvision' is a keyword corresponding to the company of the profile information. Automatic extraction is possible.
  • the generation module 170 when the generation module 170 recognizes even'Elvy', a matching rate of 66% with the pre-stored keyword'Elvision' is calculated, and thus only tags of 2 letters and 2 letters are recognized and then called'Elvision'.
  • the keyword creation module 170 may automatically recommend the keyword.
  • the reference value of the matching rate for performing automatic keyword recommendation may vary according to the number of characters and tags constituting the pre-stored keyword. For example, in the case of a keyword composed of 3 letters, if the letters and tags are the same as up to 2 letters, 66% of automatically recommending the keyword can be set as a reference value. It is possible to set 60% to automatically recommend keywords as a reference value.
  • FIG. 2 is a view showing in more detail the data processing block of the profile information generation server according to the embodiment
  • FIG. 3 is a view for explaining the machine learning process of the profile information generation server according to the embodiment.
  • the database of the profile information generation server may be composed of a keyword storage unit 131, a tag storage unit 133, a profile preliminary information storage unit 135, the extraction module 150
  • the learning unit 151, the extraction unit 153 and the tagging unit 155 may be configured, and the generation module 170 may include a generation unit 171, a classification unit 173, and an output unit 175.
  • the calculation module 190 may be configured to include a counting unit 191 and the calculation unit 193.
  • the tag storage unit 133 stores detailed item information of the profile information.
  • the tag storage unit stores category information constituting profile information such as job, age, date of birth, affiliation, institution, position, career, peculiarity, address, job, annual sales.
  • the profile preliminary information storage unit 135 stores profile preliminary information tagged with letters constituting a keyword.
  • the learning unit 151 of the extraction module 150 analyzes the meaning of the words included in the sentence and the location information in the sentence of the word to infer the meaning and correlation between words, and machine learning to extract profile preliminary information To perform.
  • a model of machine learning may be trained to enable Named Entity Recognition (hereinafter NER).
  • NER Named Entity Recognition
  • the generation module 170 may use tagging information of letters adjacent to a specific letter to correct the tagging error of the specific letter constituting the word. For example, as a result of analyzing the remaining tags excluding'last name' and'first name' in the input sentence, when two or more consecutive tags do not appear, the generation module 170 displays the tags of the surrounding letters that are the first letter and the last letter of the specific letter. Recognize. If the tags of the front and back letters, which are the recognized surrounding letters, are the same type of tag, the tags of the specific letters, which are intermediate letters, are changed to the same tags as the tags of the front letters and the back letters. Afterwards, a keyword including the text with the changed tag is generated.
  • the generation module 170 may change B to tag1 and recognize'ABCDE' as tag1. Through this, it is possible to lower the error rate of profile generation due to tagging error.
  • the extraction module receives profile pre-word data tagged with keywords and classifications from the database. Thereafter, a model for profile information is generated through a training process using the transmitted data.
  • various neural networks including LSTM (RNN) and CNN may be used. Subsequently, prediction on a new input is performed based on the generated model. That is, the extraction unit 151 automatically extracts keywords when a document is input according to the result of machine learning.
  • the tagging unit 155 assigns a tag indicating the category or metadata of the keyword to each letter included in the extracted keyword. In an embodiment, when another word adjacent to the keyword is a tag indicating profile category information, it may be added to each letter of the keyword.
  • the generation module 170 collects keywords tagged to each letter from the extraction module 150 and continuously merges the tagged text to generate keywords that are profile information. Thereafter, the classification unit 173 classifies the generated keyword according to the profile information category indicated by the keyword. For example, the classification unit 172 may classify according to tag information given to keywords.
  • the output unit 175 displays profile information in which keywords are sorted according to tag information.
  • the calculation module 191 may calculate profile importance according to the number of times keywords and tags are extracted from the collected document, and when a specific keyword is extracted from the profile information of the same person, reliability of the extracted keyword may be calculated. To this end, the counting unit 191 counts the number of times keywords and tags have been extracted, and the calculating unit 193 calculates keyword reliability proportional to the same keyword counting number for the same person.
  • the generation module 170 may independently generate and manage profile information for the same person, or update the profile for the same person when the profile is changed.
  • the generation module 170 compares the names in the generated profile information, and if the names are the same, compares the profile information of other categories other than the names, and if the same profile information other than the same name does not exist, a new name for the person with the same name Profile information can be created. In addition, in the embodiment, the generation module 170 may determine whether the generated profile information is the same person's profile according to a result of comparing unique information such as age and date of birth from profile information generated with the same name. If the name and unique information match, profile information of different categories is compared, and if other profile information exists, the previous profile can be updated according to the time when the profile information was generated.
  • FIG. 4 is a view for explaining a learning process of the profile information generation server and learning data of the profile information generation server according to the embodiment.
  • profile preliminary information when profile preliminary information is generated by tagging each letter, a keyword is generated by merging the letters with the same tag information consecutively, and the tag tagged to the keyword is divided into keyword category information, and b of FIG. You can create profile information such as
  • a word resulting from the use of a morpheme analyzer is generally used as a semantic unit. If the above sentence is used as the input of a morpheme analyzer,'image science' or'representative' can be selected as a word, and tags such as'major' and'position' can be assigned to the word.
  • tags such as'major' and'position' can be assigned to the word.
  • the method of tagging the morpheme is likely to generate inaccurate profile information because a proper noun, a company name with many new words, and a name are not recognized. Since the profile generation server according to the embodiment generates tag information by tagging every letter without using a morpheme analyzer, it is possible to accurately recognize important profile information such as foreign words, company names or names with many new words or proper nouns. To make.
  • FIG. 5 is a diagram illustrating a data processing flow for automatically generating profile information according to an embodiment.
  • step S510 the collection module periodically collects articles including articles, columns, and interviews from a web space including news sites and blogs on the profile auto-generation server.
  • step S530 the document is collected in a database, and the source and web space information of the document are stored, and profile generation information including a keyword for generating profile information from the document and a tag indicating a category of information including a business information and keywords is generated. To save.
  • step S550 the extraction module analyzes sentences included in the document to extract the profile information, extracts keywords from the sentences, and generates profile preliminary information by tagging profile category information in letters constituting the keyword. .
  • step S570 the generation module collects the extracted profile preliminary information, classifies the keywords according to the profile information category, generates the profile words by merging consecutively tagged letters, and collects keywords and profile words to generate profile information. .
  • step S590 display profile information is displayed according to the category of keywords and profile words.
  • FIG. 6 is a diagram illustrating a data processing process for generating profile preliminary information according to an embodiment.
  • step S551 semantic analysis of the words included in the sentence and location information in the sentence of the word are grasped to infer the semantic relationship and correlation between words, and machine learning is performed to extract profile preliminary information.
  • step S553 keywords are extracted from the input document according to the result of the machine learning.
  • step S555 profile preliminary information is generated to indicate profile information that assigns a tag indicating a category or metadata of the keyword to each letter included in the extracted keyword.
  • FIG. 7 is a view for explaining a process of generating profile information according to an embodiment.
  • the server says, “Jun Jeon Joon of the game board of a professional game board that has been over 10 years now is a person pioneering the field of domestic and global game casters.”
  • the server separates the words constituting the sentence and the letters constituting the word according to the spacing.
  • a tag is added to a letter that can indicate profile information.
  • the title tag is assigned to the letter'crab' constituting the keyword'gamecaster', and the gender tag is assigned to the letter'before' constituting the keyword'dedicated'.
  • profile preliminary information is generated by tagging each letter
  • a keyword is generated by merging the letters with the same tag information consecutively, and the tag tagged to the keyword is divided into keyword category information, and the profile shown in FIG. Information can be generated.
  • the profile information generation server and method according to the embodiment enable automatic and accurate extraction of profile information, which is important effective information about people, companies, and products from various online contents.
  • the profile information generation server and method automatically prevents the generation of incorrect profile information and the spread of information by automatically calculating the reliability of the profile information, separating profile data of the same person, and continuously updating the profile information. .

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Biomedical Technology (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention concerne un serveur et un procédé de génération de profil automatique. Un serveur de génération de profil automatique selon un mode de réalisation comprend : un module de collecte qui collecte périodiquement des documents, comprenant des articles, des colonnes et des interviews, dans un espace web comprenant des sites et des blogs d'actualité; une base de données qui stocke les documents collectés, les sources des documents, et des informations d'espace web, et stocke des informations de génération de profil comprenant des mots-clés servant à générer des informations de profil à partir des documents, et des balises représentant des catégories d'information dans lesquelles des informations commerciales et les mots-clés sont compris; un module d'extraction qui analyse des phrases comprises dans un document à partir duquel des informations de profil doivent être extraites, extrait des mots-clés, marque chacune des lettres constituant les mots-clés par des informations de balise qui sont des informations de catégorie de profil, et génère des informations de réserve de profil; et un module de génération qui collecte les informations de réserve de profil extraites, fusionne en continu des lettres marquées pour générer des mots-clés qui sont des éléments des informations de profil, et sépare les mots-clés des balises pour générer les informations de profil.
PCT/KR2019/016608 2018-11-29 2019-11-28 Serveur et procédé de génération de profil automatique WO2020111827A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR10-2018-0150670 2018-11-29
KR1020180150670A KR102185733B1 (ko) 2018-11-29 2018-11-29 프로필 자동생성서버 및 방법

Publications (1)

Publication Number Publication Date
WO2020111827A1 true WO2020111827A1 (fr) 2020-06-04

Family

ID=70853579

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2019/016608 WO2020111827A1 (fr) 2018-11-29 2019-11-28 Serveur et procédé de génération de profil automatique

Country Status (2)

Country Link
KR (1) KR102185733B1 (fr)
WO (1) WO2020111827A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102244651B1 (ko) * 2020-08-18 2021-04-26 (주)다하미 커뮤니케이션즈 뉴스 스크랩을 위한 추천 키워드 제공 시스템, 이를 이용한 뉴스 스크랩에 사용되는 키워드 사전 구성 및 업데이트 방법, 및 이를 포함하는 추천 키워드 제공방법
CN113157937A (zh) * 2021-03-04 2021-07-23 浙报融媒体科技(浙江)有限责任公司 基于流计算模型的机器实时自动写稿系统及方法

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102603206B1 (ko) * 2022-11-17 2023-11-17 퍼포마스 주식회사 마케팅을 위한 자동화된 페르소나 생성 장치 및 방법

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090319449A1 (en) * 2008-06-21 2009-12-24 Microsoft Corporation Providing context for web articles
KR20140038834A (ko) * 2012-09-21 2014-03-31 한양대학교 에리카산학협력단 웹 페이지 분석 장치 및 방법
JP2015097025A (ja) * 2013-11-15 2015-05-21 日本電信電話株式会社 プロフィール語抽出装置、プロフィール語抽出方法およびプロフィール語抽出プログラム
KR101607468B1 (ko) * 2015-02-27 2016-03-30 고려대학교 산학협력단 콘텐츠에 대한 키워드 태깅 방법 및 시스템

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100901134B1 (ko) 2008-09-09 2009-06-04 김수현 형태소 분석과 소스코드 분석을 통한 추천 태그 표시시스템
KR102529262B1 (ko) * 2017-03-20 2023-05-08 삼성전자주식회사 전자 장치 및 제어 방법

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090319449A1 (en) * 2008-06-21 2009-12-24 Microsoft Corporation Providing context for web articles
KR20140038834A (ko) * 2012-09-21 2014-03-31 한양대학교 에리카산학협력단 웹 페이지 분석 장치 및 방법
JP2015097025A (ja) * 2013-11-15 2015-05-21 日本電信電話株式会社 プロフィール語抽出装置、プロフィール語抽出方法およびプロフィール語抽出プログラム
KR101607468B1 (ko) * 2015-02-27 2016-03-30 고려대학교 산학협력단 콘텐츠에 대한 키워드 태깅 방법 및 시스템

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
KIM, HYEMIN ET AL.: "Syllable-based Korean POS Tagging using POS Distribution and Bidirectional LSTM CRFs", PROCEEDINGS OF THE 28TH ANNUAL CONFERENCE ON HUMAN AND COGNITIVE LANGUAGE TECHNOLOGY, 5 October 2016 (2016-10-05), XP055711898 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102244651B1 (ko) * 2020-08-18 2021-04-26 (주)다하미 커뮤니케이션즈 뉴스 스크랩을 위한 추천 키워드 제공 시스템, 이를 이용한 뉴스 스크랩에 사용되는 키워드 사전 구성 및 업데이트 방법, 및 이를 포함하는 추천 키워드 제공방법
CN113157937A (zh) * 2021-03-04 2021-07-23 浙报融媒体科技(浙江)有限责任公司 基于流计算模型的机器实时自动写稿系统及方法

Also Published As

Publication number Publication date
KR102185733B1 (ko) 2020-12-04
KR20200064490A (ko) 2020-06-08

Similar Documents

Publication Publication Date Title
US11048882B2 (en) Automatic semantic rating and abstraction of literature
Gu et al. " what parts of your apps are loved by users?"(T)
Kasper Sentiment analysis for hotel reviews
US20180366013A1 (en) System and method for providing an interactive visual learning environment for creation, presentation, sharing, organizing and analysis of knowledge on subject matter
US8200477B2 (en) Method and system for extracting opinions from text documents
CN109145216A (zh) 网络舆情监控方法、装置及存储介质
CN106650943A (zh) 基于人工智能的辅助写作方法和装置
WO2020111827A1 (fr) Serveur et procédé de génération de profil automatique
CN107491435A (zh) 基于计算机自动识别用户情感的方法及装置
CN112035675A (zh) 医疗文本标注方法、装置、设备及存储介质
CN109783693B (zh) 一种视频语义和知识点的确定方法及系统
Gugnani et al. Generating unified candidate skill graph for career path recommendation
US20120183935A1 (en) Learning device, determination device, learning method, determination method, and computer program product
Priya Emoji based sentiment analysis using KNN
CN114722174A (zh) 提词方法和装置、电子设备及存储介质
RU2718978C1 (ru) Способ управления автоматизированной системой правовых консультаций
Murakami et al. Statement map: assisting information crediblity analysis by visualizing arguments
Westerhout Definition extraction for glossary creation: a study on extracting definitions for semi-automatic glossary creation in Dutch
Gutiérrez et al. Sentiment classification using semantic features extracted from WordNet-based resources
CN112364068A (zh) 课程标签生成方法、装置、设备及介质
CN110008314B (zh) 一种意图解析方法及装置
Enăchescu Screening the Candidates in IT Field Based on Semantic Web Technologies: Automatic Extraction of Technical Competencies from Unstructured Resumes.
Kasper et al. Monitoring and summarization of hotel reviews
CN111949781B (zh) 一种基于自然语句句法分析的智能交互方法及装置
WO2016076622A1 (fr) Procédé de fourniture de directives en fonction d'une sélection de document, support d'enregistrement lisible par ordinateur dans lequel est enregistré un programme d'exécution dudit procédé, et application pour dispositif terminal, stockée dans un support

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19888278

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19888278

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 01/10/2021)

122 Ep: pct application non-entry in european phase

Ref document number: 19888278

Country of ref document: EP

Kind code of ref document: A1