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Mr. Ábel Elekes
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Basic Info

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Research Keywords & Expertise

1 Data Mining
1 Data Science
1 Natural Language
1 Data analysis
1 Natural langauge processing

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Career Timeline

Karlsruhe Institute of Technology

Graduate Student or Post Graduate

01 June 2016 - 01 January 2021




Short Biography

I am a researcher currently working for the Hungarian Tourism Agency.

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Journal article
Published: 08 March 2021 in Sustainability
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Despite the growing importance of mobile tracking technology in urban planning and traffic forecasting, its utilization in the understanding of the basic laws governing tourist flows remains limited. Knowledge regarding the motivations and spatial behavior of tourists has great potential in sustainable tourism studies. In this paper, we combine social media (Twitter) and mobile positioning data (MPD) in the analysis of international tourism flows in Szeged, a secondary urban center in Hungary. First, the content of geotagged and non-geotagged Twitter messages referring to Szeged in a six-month period of 2018 was analyzed. In this way specific events attracting foreign tourists were identified. Then, using MPD data of foreign SIM cards, visitor peaks in the investigated period were defined. With the joint application of the social media and mobile positioning analytical tools, we were able to identify those attractions (festivals, sport and cultural events, etc.) that generated significant tourism arrivals in the city. Furthermore, using the mixed-method approach we were also able to analyze the movements of foreign visitors during one large-scale tourism event and evaluate its hinterland. Overall, this study supports the idea that social media data should be combined with other real-time data sources, such as MPD, in order to gain a more precise understanding of the behavior of tourists. The proposed analytical tool can contribute to methodological and conceptual development in the field, and information gained by its application can positively influence not only tourism management and planning but also tourism marketing and placemaking.

ACS Style

Zoltán Kovács; György Vida; Ábel Elekes; Tamás Kovalcsik. Combining Social Media and Mobile Positioning Data in the Analysis of Tourist Flows: A Case Study from Szeged, Hungary. Sustainability 2021, 13, 2926 .

AMA Style

Zoltán Kovács, György Vida, Ábel Elekes, Tamás Kovalcsik. Combining Social Media and Mobile Positioning Data in the Analysis of Tourist Flows: A Case Study from Szeged, Hungary. Sustainability. 2021; 13 (5):2926.

Chicago/Turabian Style

Zoltán Kovács; György Vida; Ábel Elekes; Tamás Kovalcsik. 2021. "Combining Social Media and Mobile Positioning Data in the Analysis of Tourist Flows: A Case Study from Szeged, Hungary." Sustainability 13, no. 5: 2926.

Conference paper
Published: 01 June 2019 in 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
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Text classification helps to categorize large number of documents in Digital Libraries. Text classification results highly depend on the quality of labeled training data. In practice, the process of manually annotating documents is a hidden cost that is often overlooked. We propose a general preprocessing method for scenarios in which training data is scarce. It clusters semantically similar terms by including both a semantic distance measure and a probabilistic model of any task-specific term distributions. By preprocessing the training data with our method, one increases the mean classification performance of all tested classification approaches in text classification tasks having 500 or 1000 training samples. The largest observed increase is 15%. When more training samples are available, we report significant improvements in most scenarios as well.

ACS Style

Abel Elekes; Antonino Simone Di Stefano; Martin Schaler; Klemens Bohm; Matthias Keller. Learning from Few Samples: Lexical Substitution with Word Embeddings for Short Text Classification. 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2019, 111 -119.

AMA Style

Abel Elekes, Antonino Simone Di Stefano, Martin Schaler, Klemens Bohm, Matthias Keller. Learning from Few Samples: Lexical Substitution with Word Embeddings for Short Text Classification. 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL). 2019; ():111-119.

Chicago/Turabian Style

Abel Elekes; Antonino Simone Di Stefano; Martin Schaler; Klemens Bohm; Matthias Keller. 2019. "Learning from Few Samples: Lexical Substitution with Word Embeddings for Short Text Classification." 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) , no. : 111-119.

Conference paper
Published: 01 January 2018 in Proceedings of the 22nd Conference on Computational Natural Language Learning
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Word embeddings are powerful tools that facilitate better analysis of natural language. However, their quality highly depends on the resource used for training. There are various approaches relying on n-gram corpora, such as the Google n-gram corpus. However, n-gram corpora only offer a small window into the full text – 5 words for the Google corpus at best. This gives way to the concern whether the extracted word semantics are of high quality. In this paper, we address this concern with two contributions. First, we provide a resource containing 120 word-embedding models – one of the largest collection of embedding models. Furthermore, the resource contains the n-gramed versions of all used corpora, as well as our scripts used for corpus generation, model generation and evaluation. Second, we define a set of meaningful experiments allowing to evaluate the aforementioned quality differences. We conduct these experiments using our resource to show its usage and significance. The evaluation results confirm that one generally can expect high quality for n-grams with n > 3.

ACS Style

Ábel Elekes; Adrian Englhardt; Martin Schäler; Klemens Böhm. Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data. Proceedings of the 22nd Conference on Computational Natural Language Learning 2018 .

AMA Style

Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm. Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data. Proceedings of the 22nd Conference on Computational Natural Language Learning. 2018; ():.

Chicago/Turabian Style

Ábel Elekes; Adrian Englhardt; Martin Schäler; Klemens Böhm. 2018. "Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data." Proceedings of the 22nd Conference on Computational Natural Language Learning , no. : .

Conference paper
Published: 01 June 2017 in 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
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Finding similar words with the help of word embedding models, such as Google's Word2Vec or Glove, computed on large-scale digital libraries has yielded meaningful results in many cases. However, the underlying notion of similarity has remained ambiguous. In this paper, we examine when exactly similarity values in word embedding models are meaningful. To do so, we analyze the statistical distribution of similarity values systematically, conducting two series of experiments. The first one examines how the distribution of similarity values depends on the different embedding-model algorithms and parameters. The second one starts by showing that intuitive similarity thresholds do not exist. We then propose a method stating which similarity values actually are meaningful for a given embedding model. In more abstract terms, our insights give way to a better understanding of the notion of similarity in embedding models and to more reliable evaluations of such models.

ACS Style

Abel Elekes; Martin Schaeler; Klemens Boehm. On the Various Semantics of Similarity in Word Embedding Models. 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2017, 1 -10.

AMA Style

Abel Elekes, Martin Schaeler, Klemens Boehm. On the Various Semantics of Similarity in Word Embedding Models. 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL). 2017; ():1-10.

Chicago/Turabian Style

Abel Elekes; Martin Schaeler; Klemens Boehm. 2017. "On the Various Semantics of Similarity in Word Embedding Models." 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL) , no. : 1-10.