• Title, Summary, Keyword: mention

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Mention Detection Using Pointer Networks for Coreference Resolution

  • Park, Cheoneum;Lee, Changki;Lim, Soojong
    • ETRI Journal
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    • v.39 no.5
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    • pp.652-661
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    • 2017
  • A mention has a noun or noun phrase as its head and constructs a chunk that defines any meaning, including a modifier. Mention detection refers to the extraction of mentions from a document. In mentions, coreference resolution refers to determining any mentions that have the same meaning. Pointer networks, which are models based on a recurrent neural network encoder-decoder, outputs a list of elements corresponding to an input sequence. In this paper, we propose mention detection using pointer networks. This approach can solve the problem of overlapped mention detection, which cannot be solved by a sequence labeling approach. The experimental results show that the performance of the proposed mention detection approach is F1 of 80.75%, which is 8% higher than rule-based mention detection, and the performance of the coreference resolution has a CoNLL F1 of 56.67% (mention boundary), which is 7.68% higher than coreference resolution using rule-based mention detection.

Mention Detection with Pointer Networks (포인터 네트워크를 이용한 멘션탐지)

  • Park, Cheoneum;Lee, Changki
    • Journal of KIISE
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    • v.44 no.8
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    • pp.774-781
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    • 2017
  • Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

Korean Coreference Resolution with Guided Mention Pair Model Using Deep Learning

  • Park, Cheoneum;Choi, Kyoung-Ho;Lee, Changki;Lim, Soojong
    • ETRI Journal
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    • v.38 no.6
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    • pp.1207-1217
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    • 2016
  • The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately

On the Selected Blasting Method and Measurement of Vibration and Sound Level by Blasting in KU-SAN area. (구산동 아파트 재개발 사업의 발파공법 선정 및 주변 가옥에 미치는 발파 진동.소음 영향에 관한 연구)

  • 강대우
    • Explosives and Blasting
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    • v.16 no.3
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    • pp.16-24
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    • 1998
  • Methods of Rock fragmentation are used rock of housing repair development at KU-SAN DONG area in seoul Youn-Pyong Ku. So, Theorical analyses of the effect of vibration and frequency on structural damage around old housed also discussed. The results can be summarized as follows: 1. A area(Rock area not more than 15m Ku-San Mention) Some Empirical equations were obtained $V=K\{{\frac{D}{W}}1/3\}^{-n}$ where the values for n and K are estimated to be -1.64 and 94 respectively, this values were obtained only theorical analyses. If we have 125g charge this area is impossible blasting operation, so this area must be worked by SRS(Super Rock Splitter) method. 2. B area(Rock area from 15m to 25m in a boundary line from Ku-San Mention) This area charge is about 125g in a delay time by some empirical equation s. So, this area can be blasting operations by small charge. 3. C area(Rock area from 25m to 35m in a boundary line from Ku-San Mention) This area charge is about 500g in delay time by some empirical equation s. So, this area can be blasting operations by middle charge. 4. D area(Rock area more then 35m in a boundary line from Ku-San Mention) This area charge is about 1000g in a delay time by some empirical equation s. So, this area can be blasting operations by middle charge.

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Development of the comprehension of complex sentences in Korean Children (아동의 복문(複文) 이해의 발달 - 시간 절부사어의 '전'과 '후'를 중심으로 -)

  • Park, Hee Sook;Choi, Kyoung Sook
    • Korean Journal of Child Studies
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    • v.19 no.2
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    • pp.185-200
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    • 1998
  • This research examined the development in Korean children of the comprehension of complex sentences. The relative difficulty in comprehension of the temporal conjunctions "before" and "after" was investigated. The order of mention, contextual support, and syntactic appearance was controlled. The role of cognitive strategies and developmental changes in the comprehension of these conjunction was included in this study. Subjects were 90 preschool children between 3 and 5 years of age. The task was a sentence-picture matching problem having 3 types of sentences combining temporally with "before" or "after". The results were that developmental changes in comprehension of the temporal conjunctions "before' and "after" in Korean children depended on the order of mention, contextual support, and such syntactic factors as the position of the subject of the sentence. The importance of the consistency in the occurrence of events and the order of mention in the acquisition of complex sentences among Korean children is similar to the acquisition of complex sentences in other languages.

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Mention Detection using Bidirectional LSTM-CRF Model (Bidirectional LSTM-CRF 모델을 이용한 멘션탐지)

  • Park, Cheoneum;Lee, Changki
    • Annual Conference on Human and Language Technology
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    • pp.224-227
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    • 2015
  • 상호참조해결은 특정 개체에 대해 다르게 표현한 단어들을 서로 연관지어 주며, 이러한 개체에 대해 표현한 단어들을 멘션(mention)이라 하며, 이런 멘션을 찾아내는 것을 멘션탐지(mention detection)라 한다. 멘션은 명사나 명사구를 기반으로 정의되며, 명사구의 경우에는 수식어를 포함하기 때문에 멘션탐지를 순차 데이터 문제(sequence labeling problem)로 정의할 수 있다. 순차 데이터 문제에는 Recurrent Neural Network(RNN) 종류의 모델을 적용할 수 있으며, 모델들은 Long Short-Term Memory(LSTM) RNN, LSTM Recurrent CRF(LSTM-CRF), Bidirectional LSTM-CRF(Bi-LSTM-CRF) 등이 있다. LSTM-RNN은 기존 RNN의 그레디언트 소멸 문제(vanishing gradient problem)를 해결하였으며, LSTM-CRF는 출력 결과에 의존성을 부여하여 순차 데이터 문제에 더욱 최적화 하였다. Bi-LSTM-CRF는 과거입력자질과 미래입력자질을 함께 학습하는 방법으로 최근에 가장 좋은 성능을 보이고 있다. 이에 따라, 본 논문에서는 멘션탐지에 Bi-LSTM-CRF를 적용할 것을 제안하며, 각 딥 러닝 모델들에 대한 비교실험을 보인다.

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Analysis of the effect of the mention in SNS on the result of election (SNS의 관심도가 선거결과에 미치는 영향 분석)

  • Choi, Eun-Jung;Choi, Sea-Won;Lee, Se-Yeon;Kim, Myhung-Joo
    • Journal of Digital Convergence
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    • v.15 no.2
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    • pp.191-197
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    • 2017
  • As individual opinions are expressed and discussed through SNS, SNS is used as a new basis to estimate the direction of public opinion. This change also appears in election. So many voters state their views through SNS, so that candidates utilize it as a new space for communication. In this paper, positive mention in SNS were collected and analysed in the course of the election of Korean 20th Congressman, to understand how the mention on election in SNS affects the result of election. This result was compared with the traditional survey on public opinion, to find out which one more corresponds to the result. In conclusion, mention in SNS coincide more with the result of elelction than the traditional survey.

Anaphoric Reference Resolution in Expository Text: The Effects of Ellipsis (설명문의 대용어 참조해결과정: 대용어와 지시사 생략 효과)

  • Lee, Jae-Ho
    • Korean Journal of Cognitive Science
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    • v.21 no.2
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    • pp.253-282
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    • 2010
  • Two experiments were conducted to explore the effects of anaphora and demonstrative ellipsis on reference resolution. This study assumed that two type of ellipsis could be sensitive to antecedents' saliency: the reverse typicality and mention order of antecedents. The muti-task approach measured the antecedent's activation level and processing load for the conflict resolution of theories of anaphoric resolution. In Experiment 1, using ellipsis for anaphora, participants read a series of sentence pairs by self-paced and performed a probe recognition test. The results showed the main effects of antecedent's typicality and mention order in both tasks. In Experiment 2, using noun phrase without demonstrative for anaphora, participants read a series of sentence pairs by self-paced and performed a probe recognition test. The results showed main effects of mention order of antecedents for probe recognition task only. The first antecedent was recognized faster than the second one. The results of two experiments suggested that anaphora type and antecedent's saliency were dynamically interact in reference resolution for Korean.

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Korean Co-reference Resolution End-to-End Learning using Bi-LSTM with Mention Features (언급 특질을 이용한 Bi-LSTM 기반 한국어 상호참조해결 종단간 학습)

  • Shin, Giyeon;Han, Kijong;Lee, Minho;Kim, Kuntae;Choi, Key-Sun
    • Annual Conference on Human and Language Technology
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    • pp.247-251
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    • 2018
  • 상호참조해결은 자연언어 문서 내에서 등장하는 명사구 언급(mention)과 이에 선행하는 명사구 언급을 찾아 같은 개체인지 정의하는 문제이다. 특히, 지식베이스 확장에 있어 상호참조해결은 언급 후보에 대해 선행하는 개체의 언급이 있는지 판단해 지식트리플 획득에 도움을 준다. 영어권 상호참조해결에서는 F1 score 73%를 웃도는 좋은 성능을 내고 있으나, 평균 정밀도가 80%로 지식트리플 추출에 적용하기에는 무리가 있다. 따라서 본 논문에서는 한국어 문서에 대해 영어권 상호참조해결 모델에서 사용되었던 최신 모델인 Bi-LSTM 기반의 딥 러닝 기술을 구현하고 이에 더해 언급 후보 목록을 만들어 개체명 유형과 경계를 적용하였으며 품사형태를 붙인 토큰을 사용하였다. 실험 결과, 문자 임베딩(Character Embedding) 값을 사용한 경우 CoNLL F1-Score 63.25%를 기록하였고, 85.67%의 정밀도를 보였으며, 같은 모델에 문자 임베딩을 사용하지 않은 경우 CoNLL F1-Score 67.92%와 평균 정밀도 77.71%를 보였다.

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