Electronics and Telecommunications Research Institute

Hankook Tire & Technology Co., Ltd

Korea Advanced Institute of Science and Technology

Hankook Tire Co., Ltd.

Konkuk University Industrial Cooperation Corp

Jeong Mu Heo

Myung Gil Jang

Chung Hee Lee

Hyun Ki Kim

Mi Ran Choi

Yong Jin Bae

Soo Jong Lim

Joon Ho Lim

Pum Mo Ryu

Yeo Chan Yoon

Hyo Jung Oh

Hyung Jik Lee

Myung-Gil Jang

Hyun-Ki Kim

Soo-Jong Lim

Hyo-Jung Oh

Sang Kyu Park

Soojong Lim

Mi-Ran Choi

Yeo-Chan Yoon

Miran Choi

Hyunki Kim

Ji Hyun Wang

Seok Ju Choi

Changki Lee

Yoon Jae Choi

Hee-Cheol Seo

Kyung Man Bae

Yo Han Jo

Sung Hee Youn

Jeong Heon Kim

Min Ho Kim

Hyeon Jin Kim

Myong Joong Lee

Chung-hee Lee

Kyoung-Ro Yoon

Tek Jin Nam

Se Yeong Kim

Oo Sung Son

Dong Hyeok Yun

Kwang Eun Park

Daniel Saakes

Gi Tae Koo

YiGyu Hwang

Yong-Jin Bae

Yi-Gyu Hwang

Jeong Yeon Lee

Seung Ryeol Kim

Jae Wook Ihm

Yea Kyung Row

Ji Hee Ryu

Hyun Soo Kim

Hyun Suk Kim

Ki Young Lee

Young Kil Kim

Sang-Kyu Park

Soon Hong So

Young-Ae Seo

Jong Hun Shin

Soo-Jun Park

Hyung-Jik Lee

Chang Ki Lee

Min-Ho Kim

Yi Gyu Hwang

Joon-Ho Lim

Chang-Ki Lee

Young Rae Kim

Ji Ae Shin

Chang Young Sohn

Ji-Hee Ryu

Kyung-Man Bae

Jihee Ryu

Jin Seong

Yoonjae Choi

Pum-Mo Ryu

Yoon-Jae Choi

Yo-Han Jo

Kyung Hwa Lee

Provided is a method of generating a language model using crossmodal information. The method includes: receiving language-based first modality information and non-language-based second modality information; converting the first modality information into a first byte sequence; converting the second modality information into a second byte sequence; converting the first and second byte sequences into a first embedding vector and a second embedding vector by applying an embedding technique for each modality; generating semantic association information between first and second modality information by inputting the first and second embedding vectors to a crossmodal transformer; and learning the language model by setting the generated semantic association information as training data.

Method and apparatus for generating language model using crossmodal information

The present invention relates to an apparatus and method for automatically generating machine reading comprehension training data, and more particularly, to an apparatus and method for automatically generating and managing machine reading comprehension training data based on text semantic analysis. The apparatus for automatically generating machine reading comprehension training data according to the present invention includes a domain selection text collection unit configured to collect pieces of text data according to domains and subjects, a paragraph selection unit configured to select a paragraph using the pieces of collected text data and determine whether questions and correct answers are generatable, and a question and correct answer generation unit configured to generate questions and correct answers from the selected paragraph.

Apparatus and method for automatic generation of machine reading comprehension training data

Disclosed herein are an apparatus and method for providing a search service based on important sentences. The apparatus for providing a search service based on important sentences includes memory in which at least one program and a previously trained word importance measurement model are recorded and a processor for executing the program. The program may include a word importance measurement unit for measuring the importance of each of multiple words included in input text in the corresponding input text based on the word importance measurement model and a sentence importance measurement unit for measuring the importance of each of at least one sentence included in the text based on the measured importance of each of the multiple words.

Apparatus and method for providing indexing and search service based on important sentence

A non-pneumatic tire having a block type reinforcement configuration includes: a tread unit that forms an outer portion of the tire and comes in contact with a road surface; a wheel unit that is connected to an axle; a spoke unit that is formed between the tread unit and the wheel unit; and a block type reinforcement unit that includes a block formed inside the tread unit in the circumferential direction of the tread unit, and a plurality of reinforcing members formed in the block to correspond to the shape of the block.

Non-pneumatic tire having block type reinforcement

The disclosed technology generally relates to a tire and more particularly to a non-pneumatic tire. The non-pneumatic tire that has maneuverability similar to a pneumatic tire even without using air pressure, and can support the load of a vehicle and can guide performance-oriented tire deformation behavior by using a meta structure. In an aspect, a non-pneumatic tire includes a tread that comes in contact with the ground, a rim that is connected with an axle, and a spoke unit that serves as a structural support between the rim and the tread, in which the spoke unit is formed in a meta structure in which unit structures are repeated.

Non-pneumatic tire

The disclosed technology generally relates to a tire, and more particularly to a non-pneumatic tire that has handling performance similar to a pneumatic tire, supports the load of a vehicle and improves riding comfort by employing a parametric structure. In one aspect, a non-pneumatic tire includes a tread that comes in contact with the ground, a rim that is connected with an axle, and a spoke unit that functions as a structural support between the rim and the tread, in which the spoke unit is formed by circularly arranging spokes. The spokes have a single closed-loop longitudinal cross-section circumscribed to the rim and around the center of the tire.

A chatbot system and method conversing with other chatbots in place of a user. The chatbot system and method understand the purpose of conversation of a user, select chatbots to which the purpose of the user is to be transferred, converse with the selected chatbots in place of the user, and present the user with a result of conversation undertaken. The chatbot system converses with provider chatbots provided by property and service providers. The chatbot system includes a consumer chatbot that understands a purpose of conversation of a user, converses with the provider chatbots in place of the user, and provides a result of the conversation to the user.

Chatbot system and service method thereof

The language analysis method includes collecting an online document, performing coined word filtering on the collected document based on a predetermined coined word extraction pattern and a dictionary to extract a coined word candidate which corresponds to the coined word extraction pattern and is not registered in a coined stop word dictionary, performing morpheme analysis on the extracted coined word candidate to verify a coined word candidate which does not correspond to a predetermined part of speech and is not registered in a pre-stored morpheme dictionary, refining the coined word extraction pattern, based on a result of the verification and updating the verified coined word candidate in the coined stop word dictionary, and updating the verified coined word candidate in the morpheme dictionary, based on the result of the verification.

Online contextual advertisement intellectualization apparatus and method based on language analysis for automatically recognizing coined word

Provided are an interactive question answering apparatus and method, which improve an unnatural question sentence and prevent the impossibility of a question in a state which does not know additional information, thereby providing a multimodal-based question answering method enabling a person to provide a question like conversing. Also, a question answering service is exposed to potential product purchasers of content such as an image, video, audio, etc., thereby providing an environment available to advertisement market.

Interactive question-answering apparatus and method thereof

Provided are a system and method for answering a natural language question which show improved information retrieval performance. The system includes an index unit configured to analyze text of previously stored irregular documents and classify and index the irregular documents according to meanings of sentences or paragraphs, a database configured to receive and store the irregular documents indexed according to the meanings and transmitted from the index unit, a retrieval unit configured to extract an index word by semantically analyzing an input question and search the database for documents related to the extracted index word, and a provision unit configured to generate a correct answer to the question by analyzing the documents searched by the retrieval unit and provide the search results and the correct answer.

System and method for answering natural language question

An apparatus for amending a language analysis error includes: a main language analyzer, which includes a plurality of language processing modules being sequentially connected to each other, and generates one best main analysis result for each processing module; a subsidiary language analyzer, which includes the plurality of language processing modules, and generates a plurality of subsidiary analysis results for each of the plurality of language processing modules; and an analysis result amender to in response to an error occurring in the main analysis result, acquire a subsidiary analysis result, and transmit the subsidiary analysis result to the main language analyzer.

Apparatus and method for amending language analysis error

The present invention is to provide an apparatus for searching information based on Wikipedia's contents comprising: a document converting part extracting fulltext documents, section title documents, info-box documents, category documents and definition statement documents from Wikipedia original documents and generating at least one of Wikipedia documents for questions and answers; a document indexing part analyzing the Wikipedia document for questions and answers, extracting POS-based index terms from the Wikipedia document for questions and answers, and generating a Wikipedia document index for questions and answers; a question analyzing part receiving a natural language question, analyzing a question pattern, an answer pattern and a question focus from the natural language question, and extracting document search keywords; a document searching part performing document search by using the document search keywords from the Wikipedia document index for questions and answers and generating document search result from each Wikipedia document index for questions and answers; an answer extracting part extracting first answers by using information about the question pattern, the answer pattern and the question focus from the document search result; and an answer integrating part integrating and prioritizing the first answer and generating a second answer.

Apparatus and method for searching information based on Wikipedia's contents

The disclosed technology generally relates to tires, and more particularly to a method of and a smart tire system for evaluating a state of a tire, and providing an alarm, and to a smart tire configured for the method and the system. In one aspect, a method includes determining whether an abnormality or a problem exists in a state of a tire, by processing a tire state value from a TPMS sensor. Processing the tire state can be carried out by a sensor processor and/or a sensor information processor. The method additionally includes providing an alarm to a user, e.g., a driver. The method additionally includes verifying the determination of whether there is an abnormality or a problem. Thus, the disclosed method includes determining the state of a tire and providing an alarm, where the alarm has high reliability because it is generated based on examination of various parameters of the tire state, including, e.g., pressure, temperature and/or acceleration. The disclosed technology additionally relates to a system and a smart tire configured to implement the method. The method, the system and the tire according to embodiments may be improvements over existing methods, systems and tires.

Method and system for evaluating tire state and tire configured for same

Disclosed are a question answering system for structured knowledgebase using deep natural language question analysis, and a method thereof, the question answering system for structured knowledgebase using deep natural language question analysis includes a deep natural language question analysis unit configured to create a structure of a semantic frame by analyzing a natural language question that is input, a question-intermediate expression creation unit configured to create a question-intermediate expression of a lexicon level based on the semantic frame, a knowledgebase-specialized query creation unit configured to create a query used to search in knowledgebase that is a subject of search, based on the question-intermediate expression, and a knowledgebase search unit configured to find a correct answer in the knowledgebase that is subject of search based on the query, to provide an accuracy of the correct answer, a confidence of the correct answer and an evidence for the correct answer.

Question answering system and method for structured knowledgebase using deep natural language question analysis

A natural language question answering system and method, and a paraphrase module are provided. The natural language question answering system includes a conversion module configured to generate a plurality of modified questions by paraphrasing a user's question; a plurality of question answering engines configured to receive each of the user's question and the modified questions, and select candidate answers corresponding to each of the user's question and the modified questions; and a detection module configured to detect at least one among the selected candidate answers as an answer.

Natural language question answering system and method, and paraphrase module

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Daejeon, South Korea

Jeong Mu Heo