Development of semi-supervised named entity recognition to discover new tourism places

Saputro, Khurniawan Eko; Kusumawardani, Sri Suning; Fauziati, Silmi

doi:10.1109/icstc.2016.7877360

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…No domínio do turismo, onde se situa a nossa proposta, a identificação de entidades mencionadas também foi ganhando popularidade, embora outras técnicas como topic modelling ou a análise de sentimentos continuem a ser as mais utilizadas (Egger, 2022). A língua de trabalho é maioritariamente o inglês (Saputro et al, 2016;Vijay & Sridhar, 2016;Chantrapornchai & Tunsakul, 2019), embora também tenham sido desenvolvidos recursos e sistemas de NER para outras línguas, como o chinês (Guo et al, 2009;Xue et al, 2019), o mongol (Cheng et al, 2020), o português (Matos et al, 2021), o espanhol (García-Pablos et al, 2015) ou o árabe (Bouabdallaoui et al, 2022). Muitos dos sistemas produzidos utilizam corpora extraídos de avaliações de clientes em portais web ou redes sociais, mas não existem corpora anotados de entrevistas a visitantes, especialmente num subdomínio tão específico como o Caminho de Santiago.…”

Section: Trabalho Relacionadounclassified

Desenvolvimento e avaliação de um modelo NER no domínio da análise cultural e do turismo

Sotelo Docío,

Gamallo,

Iriarte

2023

Linguamática

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O Reconhecimento de Entidades Mencionadas (NER) é uma tarefa essencial de extracção de informação em que as entidades de um texto são identificadas e classificadas. Um dos principais desafios enfrentados pelos sistemas NER é a dificuldade de generalização do aprendido para outros tipos de corpora diferentes dos utilizados durante o treino. Este problema é acentuado pelo facto de a maioria dos corpora de treino utilizados serem de natureza jornalística e, portanto, precisarem de ser adaptados a outros géneros e domínios. Neste artigo, utilizamos um corpus espanhol composto por entrevistas a visitantes da cidade de Santiago de Compostela e anotado com entidades mencionadas, para a avaliação e treino de sistemas NER adaptados ao domínio da cultura e do turismo. Apresentamos uma comparação das diferentes abordagens aplicadas, desde algoritmos clássicos de aprendizagem automática ao afinamento de vários modelos de Transformers. Os resultados obtidos superam significativamente o baseline, representado aqui pelos toolkits Stanza, spaCy e Flair, embora os testes preliminares com entidades não observadas durante o treino sugiram a necessidade de avaliações adicionais da sua capacidade de generalização e o uso de um método de segmentação adversarial no corpus.

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Section: Trabalho Relacionadounclassified

Desenvolvimento e avaliação de um modelo NER no domínio da análise cultural e do turismo

Sotelo Docío,

Gamallo,

Iriarte

2023

Linguamática

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“…In [96] naive Bayes, SVMs and gradient boosting are used as methodologies for developing a feasible classifier for a hotel recommendation system. Authors in [99] developed a support system to recognize tourism places on the web pages based on naive Bayes. SVR is also used in [44], combined with ANFIS to enhance the predictive accuracy of a recommender system along various techniques for clustering.…”

Section: Tourism Recommender Systemsmentioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.

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“…NER has been used in various works in the tourism domain ( Cheng et al, 2020 ; Vijayakrishna & Sobha, 2008 ; Saputro, Kusumawardani & Fauziati, 2016 ; Hu, Nuo & Tang, 2019 ; Chantrapornchai & Tunsakul, 2021 ; Xue et al, 2019 ). CRF was used in Vijayakrishna & Sobha (2008) to perform tourism named entity recognition using Tamil dataset which obtained an F1-score of 80.44%.…”

Section: Introductionmentioning

confidence: 99%

“…CRF was used in Vijayakrishna & Sobha (2008) to perform tourism named entity recognition using Tamil dataset which obtained an F1-score of 80.44%. A semi-supervised NER was developed in Saputro, Kusumawardani & Fauziati (2016) to find new tourist attractions. However, another two-stage idea (YATSI), naive Bayes classifier (NBC), and K-nearest neighbor (KNN) are the methodologies used in that research and it obtained an average F1-score of 69%.…”

Section: Introductionmentioning

confidence: 99%

BERT-based tourism named entity recognition: making use of social media for travel recommendations

Fudholi,

Zahra,

Rani

et al. 2023

PeerJ Computer Science

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Background Social media has become a massive encyclopedia of almost anything due to its content richness. People tell stories, write comments and feedback, and share knowledge through social media. The information available on social media enables ‘clueless’ travelers to get quick travel recommendations in the tourism sector. Through a simple query, such as typing ‘places to visit in Bali’, travelers can get many blog articles to help them decide which places of interest to visit. However, doing this reading task without a helper can be overwhelming. Methods To overcome this problem, we developed Bidirectional Encoder Representations from Transformers (BERT)-based tourism named entity recognition system, which is used to highlight tourist destination places in the query result. BERT is a state-of-the-art machine learning framework for natural language processing that can give a decent performance in various settings and cases. Our developed tourism named entity recognition (NER) model specifies three different tourist destinations: heritage, natural, and purposefully built (man-made or artificial). The dataset is taken from various tourism-related community articles and posts. Results The model achieved an average F1-score of 0.80 and has been implemented into a traveling destination recommendation system. By using this system, travelers can get quick recommendations based on the popularity of places visited in the query frame. Discussion Based on the survey that we conducted to target respondents who have never visited and have no or limited knowledge about tourist attractions in some example cities, their average interest level from the recommendation results is higher than four on a scale of 1 to 5. Thus, it can be considered a good recommendation. Furthermore, the NER model performance is comparable to another related research.

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Development of semi-supervised named entity recognition to discover new tourism places

Cited by 10 publications

References 9 publications

Desenvolvimento e avaliação de um modelo NER no domínio da análise cultural e do turismo

Desenvolvimento e avaliação de um modelo NER no domínio da análise cultural e do turismo

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

BERT-based tourism named entity recognition: making use of social media for travel recommendations

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