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  • Open Access English
    Authors: 
    Jungwon Yoon; Jihye Lee;
    Publisher: Multidisciplinary Digital Publishing Institute

    Apartments were crucial solutions to provide sufficient dwellings and to improve residential environment quality in the period after the Korean War. Thirty years after the first rush of apartment construction, many of those apartments have been demolished. However, several small-scale apartment complexes or single-building apartments without collective estates were not included in reconstruction efforts due to property, ownership, and reconstruction feasibility issues. Four such apartments remain in the Seoul Station Urban Regeneration Area. Although they are considered severely deteriorated, their architectural, historical, and cultural heritage values warrant inclusion in the Seoul Future Heritage list. From the perspective of urban regeneration, these apartments should be targeted for revitalization not only to preserve their originality but to improve the quality of sustainable building conditions and operations. In this study, we examine Choongjeong Apartment, Hoehyeon Civic Apartment, St.Joseph Apartment, and Seosomun Apartment in terms of balance among six heritage values and their improvement needs, as well as possible revitalization strategies that support sustainable urban regeneration in the area. We argue that their physical conditions can be brought up to applicable building codes, if financial support is forthcoming and numerous decision-makers allow. However, sustainable revitalization of apartments requires examination of factors affecting adaptive reuse. Through a literature and data collection review within an analysis framework, we analyze factors and issues for adaptive reuse of the four apartments. It is expected that the findings of this paper will provide insight into the role of various actors determining and taking actions for strategic physical interventions and change of uses.

  • Publication . Conference object . Preprint . Article . 2020 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Akari Asai; Jungo Kasai; Jonathan H. Clark; Kenton Lee; Eunsol Choi; Hannaneh Hajishirzi;
    Publisher: arXiv
    Project: NSF | CAREER: Learning Scalable... (1252835)

    Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where languages have few reference articles -- and information asymmetry -- where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on questions from TyDi QA lacking same-language answers. Our task formulation, called Cross-lingual Open Retrieval Question Answering (XOR QA), includes 40k information-seeking questions from across 7 diverse non-English languages. Based on this dataset, we introduce three new tasks that involve cross-lingual document retrieval using multi-lingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at https://nlp.cs.washington.edu/xorqa. Comment: Published as a conference paper at NAACL-HLT 2021 (long)

  • Open Access
    Authors: 
    Glenn Roe; Clovis Gladstone; Robert Morrissey;
    Publisher: Frontiers Media SA
    Country: France

    This article describes the use of latent Dirichlet allocation (LDA), or topic modeling, to explore the discursive makeup of the eighteenth century Encyclopédie of Denis Diderot and Jean le Rond d’Alembert (1751–1772). Expanding upon previous work modeling the Encyclopédie’s ontology, or classification scheme, we examine the abstractions used by its editors to visualize the various “systems” of knowledge that the work proposes, considered here as heuristic tools for navigating the complex information space of the Encyclopédie. Using these earlier experiments with supervised machine-learning models as a point of reference, we introduce the notion of topic modeling as a “discourse analysis tool” for Enlightenment studies. In so doing, we draw upon the tradition of post-structuralist French discourse analysis, one of the first fields to embrace computational approaches to discursive text analysis. Our particular use of LDA is thus aimed primarily at uncovering interdisciplinary “discourses” in the Encyclopédie that run alongside, under, above, and through the original classifications. By mapping these discourses and discursive practices, we can begin to move beyond the organizational (and physical) limitations of the print edition, suggesting several possible avenues of future research. These experiments thus attest once again to the enduring relevance of the Encyclopédie as an exemplary Enlightenment text. Its rich dialogical structure, whether studied using traditional methods of close reading or through the algorithmic processes described in this article, is perhaps only now coming fully to light thanks to recent developments in digital resources and methods.

  • Publication . Conference object . Other literature type . 2017
    Open Access
    Authors: 
    Han, Ting; Schlangen, David;
    Publisher: Association for Computational Linguistics
    Country: Germany

    Grounded semantics is typically learnt from utterance-level meaning representations (e.g., successful database retrievals, denoted objects in images, moves in a game). We explore learning word and utterance meanings by continuous observation of the actions of an instruction follower (IF). While an instruction giver (IG) provided a verbal description of a configuration of objects, IF recreated it using a GUI. Aligning these GUI actions to sub-utterance chunks allows a simple maximum entropy model to associate them as chunk meaning better than just providing it with the utterance-final configuration. This shows that semantics useful for incremental (word-by-word) application, as required in natural dialogue, might also be better acquired from incremental settings.

  • Authors: 
    Mondher Bouazizi; Tomoaki Ohtsuki;
    Publisher: IEEE

    Most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented towards the classification of texts into positive and negative. In this paper, we propose a pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets). We classify the tweets into 7 different classes; however the approach can be run to classify into more classes. Experiments show that our approach reaches an accuracy of classification equal to 56.9% and a precision level of sentimental tweets (other than neutral and sarcastic) equal to 72.58%. Nevertheless, the approach proves to be very accurate in binary classification (i.e., classification into “positive” and “negative”) and ternary classification (i.e., classification into “positive”, “negative” and “neutral”): in the former case, we reach an accuracy of 87.5% for the same dataset used after removing neutral tweets, and in the latter case, we reached an accuracy of classification of 83.0%.

  • Publication . Other literature type . Article . 2018
    Open Access English
    Authors: 
    Ekaterina Egorova; Ludovic Moncla; Mauro Gaio; Christophe Claramunt; Ross S. Purves;
    Publisher: HAL CCSD
    Countries: France, Switzerland, France, France

    International audience; Fictive motion (e.g. 'The highway runs along the coast') is a pervasive phenomenon in language that can imply both a static and a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclo-paedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse

  • Publication . Conference object . Preprint . Article . 2019
    Open Access
    Authors: 
    Jingqing Zhang; Piyawat Lertvittayakumjorn; Yike Guo;
    Country: United Kingdom

    Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario. Comment: Accepted NAACL-HLT 2019

  • Open Access
    Authors: 
    Fan Bu;
    Publisher: IOP Publishing

    In modern manufacturing industries, machines are connected and shared within the manufacturing network. Thus, IoT (Internet of Things) and information technologies are widely applied in the smart factory, among which RFID (Radio Frequency Identification) devices are the most important elements to collect real time data and track important objects. With the continuously developing and widely applications of RFID technology, huge amount of RFID data would be generated during manufacturing processes. In this paper, an intelligent factory framework based on RFID is proposed and massive RFID data is produced. Then the unique characteristics of RFID data in intelligent factory are analyzed, and an algorithm of mining frequent patterns based on Apriori is designed to mine the frequent path knowledge. The algorithm is helpful for the production planning and task scheduling of intelligent factory.

  • Closed Access
    Authors: 
    Christoph M. Wilk; Shigeki Sagayama;
    Publisher: IEEE

    In this paper, we present a new algorithm for automatic music completion. We have proposed automatic music completion as the class of music composition assistance problems of generating a complete piece of music given fragments of musical ideas input by a user. These fragments include partial melodies in multiple voices or parts of the underlying harmony progression. Therefore, it is a generalization of common problems such as melody harmonization or harmony constrained melody generation, but also includes problems with constraints in multiple domains, i.e. multiple voices and harmony. We present a new polyphonic voicing model for automatically completing four-part chorales. It is based on a hidden Markov model and what we call correction factors. These factors are trainable functions that efficiently capture the context of a voicing in order to account for a multitude of music theoretical rules without having to resort to a rule-based system. We observed improvement over our old model with regards to metrics that are derived from music theory for polyphonic voicing, and also invite the reader to try our algorithm themselves at http://160.16.202.131/music_completion_apsipa.

  • Open Access
    Authors: 
    Augustine Yongwhi Kim; Jin Gwan Ha; Hoduk Choi; Hyeonjoon Moon;
    Publisher: Hindawi Limited

    The purpose of this paper is to evaluate food taste, smell, and characteristics from consumers’ online reviews. Several studies in food sensory evaluation have been presented for consumer acceptance. However, these studies need taste descriptive word lexicon, and they are not suitable for analyzing large number of evaluators to predict consumer acceptance. In this paper, an automated text analysis method for food evaluation is presented to analyze and compare recently introduced two jjampong ramen types (mixed seafood noodles). To avoid building a sensory word lexicon, consumers’ reviews are collected from SNS. Then, by training word embedding model with acquired reviews, words in the large amount of review text are converted into vectors. Based on these words represented as vectors, inference is performed to evaluate taste and smell of two jjampong ramen types. Finally, the reliability and merits of the proposed food evaluation method are confirmed by a comparison with the results from an actual consumer preference taste evaluation.

search
Include:
The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
34,848 Research products, page 1 of 3,485
  • Open Access English
    Authors: 
    Jungwon Yoon; Jihye Lee;
    Publisher: Multidisciplinary Digital Publishing Institute

    Apartments were crucial solutions to provide sufficient dwellings and to improve residential environment quality in the period after the Korean War. Thirty years after the first rush of apartment construction, many of those apartments have been demolished. However, several small-scale apartment complexes or single-building apartments without collective estates were not included in reconstruction efforts due to property, ownership, and reconstruction feasibility issues. Four such apartments remain in the Seoul Station Urban Regeneration Area. Although they are considered severely deteriorated, their architectural, historical, and cultural heritage values warrant inclusion in the Seoul Future Heritage list. From the perspective of urban regeneration, these apartments should be targeted for revitalization not only to preserve their originality but to improve the quality of sustainable building conditions and operations. In this study, we examine Choongjeong Apartment, Hoehyeon Civic Apartment, St.Joseph Apartment, and Seosomun Apartment in terms of balance among six heritage values and their improvement needs, as well as possible revitalization strategies that support sustainable urban regeneration in the area. We argue that their physical conditions can be brought up to applicable building codes, if financial support is forthcoming and numerous decision-makers allow. However, sustainable revitalization of apartments requires examination of factors affecting adaptive reuse. Through a literature and data collection review within an analysis framework, we analyze factors and issues for adaptive reuse of the four apartments. It is expected that the findings of this paper will provide insight into the role of various actors determining and taking actions for strategic physical interventions and change of uses.

  • Publication . Conference object . Preprint . Article . 2020 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Akari Asai; Jungo Kasai; Jonathan H. Clark; Kenton Lee; Eunsol Choi; Hannaneh Hajishirzi;
    Publisher: arXiv
    Project: NSF | CAREER: Learning Scalable... (1252835)

    Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where languages have few reference articles -- and information asymmetry -- where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on questions from TyDi QA lacking same-language answers. Our task formulation, called Cross-lingual Open Retrieval Question Answering (XOR QA), includes 40k information-seeking questions from across 7 diverse non-English languages. Based on this dataset, we introduce three new tasks that involve cross-lingual document retrieval using multi-lingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at https://nlp.cs.washington.edu/xorqa. Comment: Published as a conference paper at NAACL-HLT 2021 (long)

  • Open Access
    Authors: 
    Glenn Roe; Clovis Gladstone; Robert Morrissey;
    Publisher: Frontiers Media SA
    Country: France

    This article describes the use of latent Dirichlet allocation (LDA), or topic modeling, to explore the discursive makeup of the eighteenth century Encyclopédie of Denis Diderot and Jean le Rond d’Alembert (1751–1772). Expanding upon previous work modeling the Encyclopédie’s ontology, or classification scheme, we examine the abstractions used by its editors to visualize the various “systems” of knowledge that the work proposes, considered here as heuristic tools for navigating the complex information space of the Encyclopédie. Using these earlier experiments with supervised machine-learning models as a point of reference, we introduce the notion of topic modeling as a “discourse analysis tool” for Enlightenment studies. In so doing, we draw upon the tradition of post-structuralist French discourse analysis, one of the first fields to embrace computational approaches to discursive text analysis. Our particular use of LDA is thus aimed primarily at uncovering interdisciplinary “discourses” in the Encyclopédie that run alongside, under, above, and through the original classifications. By mapping these discourses and discursive practices, we can begin to move beyond the organizational (and physical) limitations of the print edition, suggesting several possible avenues of future research. These experiments thus attest once again to the enduring relevance of the Encyclopédie as an exemplary Enlightenment text. Its rich dialogical structure, whether studied using traditional methods of close reading or through the algorithmic processes described in this article, is perhaps only now coming fully to light thanks to recent developments in digital resources and methods.

  • Publication . Conference object . Other literature type . 2017
    Open Access
    Authors: 
    Han, Ting; Schlangen, David;
    Publisher: Association for Computational Linguistics
    Country: Germany

    Grounded semantics is typically learnt from utterance-level meaning representations (e.g., successful database retrievals, denoted objects in images, moves in a game). We explore learning word and utterance meanings by continuous observation of the actions of an instruction follower (IF). While an instruction giver (IG) provided a verbal description of a configuration of objects, IF recreated it using a GUI. Aligning these GUI actions to sub-utterance chunks allows a simple maximum entropy model to associate them as chunk meaning better than just providing it with the utterance-final configuration. This shows that semantics useful for incremental (word-by-word) application, as required in natural dialogue, might also be better acquired from incremental settings.

  • Authors: 
    Mondher Bouazizi; Tomoaki Ohtsuki;
    Publisher: IEEE

    Most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented towards the classification of texts into positive and negative. In this paper, we propose a pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets). We classify the tweets into 7 different classes; however the approach can be run to classify into more classes. Experiments show that our approach reaches an accuracy of classification equal to 56.9% and a precision level of sentimental tweets (other than neutral and sarcastic) equal to 72.58%. Nevertheless, the approach proves to be very accurate in binary classification (i.e., classification into “positive” and “negative”) and ternary classification (i.e., classification into “positive”, “negative” and “neutral”): in the former case, we reach an accuracy of 87.5% for the same dataset used after removing neutral tweets, and in the latter case, we reached an accuracy of classification of 83.0%.

  • Publication . Other literature type . Article . 2018
    Open Access English
    Authors: 
    Ekaterina Egorova; Ludovic Moncla; Mauro Gaio; Christophe Claramunt; Ross S. Purves;
    Publisher: HAL CCSD
    Countries: France, Switzerland, France, France

    International audience; Fictive motion (e.g. 'The highway runs along the coast') is a pervasive phenomenon in language that can imply both a static and a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclo-paedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse

  • Publication . Conference object . Preprint . Article . 2019
    Open Access
    Authors: 
    Jingqing Zhang; Piyawat Lertvittayakumjorn; Yike Guo;
    Country: United Kingdom

    Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario. Comment: Accepted NAACL-HLT 2019

  • Open Access
    Authors: 
    Fan Bu;
    Publisher: IOP Publishing

    In modern manufacturing industries, machines are connected and shared within the manufacturing network. Thus, IoT (Internet of Things) and information technologies are widely applied in the smart factory, among which RFID (Radio Frequency Identification) devices are the most important elements to collect real time data and track important objects. With the continuously developing and widely applications of RFID technology, huge amount of RFID data would be generated during manufacturing processes. In this paper, an intelligent factory framework based on RFID is proposed and massive RFID data is produced. Then the unique characteristics of RFID data in intelligent factory are analyzed, and an algorithm of mining frequent patterns based on Apriori is designed to mine the frequent path knowledge. The algorithm is helpful for the production planning and task scheduling of intelligent factory.

  • Closed Access
    Authors: 
    Christoph M. Wilk; Shigeki Sagayama;
    Publisher: IEEE

    In this paper, we present a new algorithm for automatic music completion. We have proposed automatic music completion as the class of music composition assistance problems of generating a complete piece of music given fragments of musical ideas input by a user. These fragments include partial melodies in multiple voices or parts of the underlying harmony progression. Therefore, it is a generalization of common problems such as melody harmonization or harmony constrained melody generation, but also includes problems with constraints in multiple domains, i.e. multiple voices and harmony. We present a new polyphonic voicing model for automatically completing four-part chorales. It is based on a hidden Markov model and what we call correction factors. These factors are trainable functions that efficiently capture the context of a voicing in order to account for a multitude of music theoretical rules without having to resort to a rule-based system. We observed improvement over our old model with regards to metrics that are derived from music theory for polyphonic voicing, and also invite the reader to try our algorithm themselves at http://160.16.202.131/music_completion_apsipa.

  • Open Access
    Authors: 
    Augustine Yongwhi Kim; Jin Gwan Ha; Hoduk Choi; Hyeonjoon Moon;
    Publisher: Hindawi Limited

    The purpose of this paper is to evaluate food taste, smell, and characteristics from consumers’ online reviews. Several studies in food sensory evaluation have been presented for consumer acceptance. However, these studies need taste descriptive word lexicon, and they are not suitable for analyzing large number of evaluators to predict consumer acceptance. In this paper, an automated text analysis method for food evaluation is presented to analyze and compare recently introduced two jjampong ramen types (mixed seafood noodles). To avoid building a sensory word lexicon, consumers’ reviews are collected from SNS. Then, by training word embedding model with acquired reviews, words in the large amount of review text are converted into vectors. Based on these words represented as vectors, inference is performed to evaluate taste and smell of two jjampong ramen types. Finally, the reliability and merits of the proposed food evaluation method are confirmed by a comparison with the results from an actual consumer preference taste evaluation.