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Cross-lingual few-shot hate speech and offensive language detection using meta learning
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022)
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FAIRsharing record for: General Ontology for Linguistic Description ... : GOLD ...
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Unsupervised quantification of entity consistency between photos and text in real-world news ...
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Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
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EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0
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О ЛЕКСИКО-ГРАММАТИЧЕСКИХ РАЗРЯДАХ ИМЕН СУЩЕСТВИТЕЛЬНЫХ В ТАБАСАРАНСКОМ ЯЗЫКЕ ... : ABOUT LEXICAL AND GRAMMATICAL CATEGORIES OF NOUNS IN THE TABASARAN LANGUAGE ...
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Mining an English-Chinese parallel Dataset of Financial News
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In: Journal of Open Humanities Data; Vol 8 (2022); 9 ; 2059-481X (2022)
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Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis
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In: Entropy; Volume 24; Issue 4; Pages: 510 (2022)
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The Multilingual Pragmatics of New Englishes: An Analysis of Question Tags in Nigerian English
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The phonetics and phonology of Hong Kong English: a study of fricatives
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Code: Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks ...
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Code: Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks ...
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Addressing multilingualism in the GoTriple discovery platform ...
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Addressing multilingualism in the GoTriple discovery platform ...
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The Terms of “You(s)”: How the Term of Address Used by Conversational Agents Influences User Evaluations in French and German Linguaculture ...
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'Muscles of mussels' and 'hooks of bananas' - the (incipient) numeral classifier system of Ugare (Tivoid, Cameroon/Nigeria) ...
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Towards reconstructing a Proto-Tivoid numeral classifier system ...
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Measuring Semantic Similarity of Documents by Using Named Entity Recognition Methods
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In: Masters (2022)
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Abstract:
The work presented in this thesis was born from the desire to map documents with similar semantic concepts between them. We decided to address this problem as a named entity recognition task, where we have identified key concepts in the texts we use, and we have categorized them. So, we can apply named entity recognition techniques and automatically recognize these key concepts inside other documents. However, we propose the use of a classification method based on the recognition of named entities or key phrases, where the method can detect similarities between key concepts of the texts to be analyzed, and through the use of Poincaré embeddings, the model can associate the existing relationship between these concepts. Thanks to the Poincaré Embeddings’ ability to capture relationships between words, we were able to implement this feature in our classifier. Consequently for each word in a text we check if there are words close to it that are also close to the words that make up the key phrases that we use as Gold Standard. Therefore when detecting potential close words that make up a named entity, the classifier then applies a series of characteristics to classify it. The methodology used performed better than when we only considered the POS structure of the named entities and their n-grams. However, determining the POS structure and the n-grams were important to improve the recognition of named entities in our research. By improving time to recognize similar key phrases between documents, some common tasks in large companies can have a notorious benefit. An important example is the evaluation of resumes, to determine the best professional for a specific position. This task is characterized by consuming a lot of time to find the best profiles for a position, but our contribution in this research work considerably reduces that time, finding the best profiles for a job. Here the experiments are shown considering job descriptions and real resumes, and the methodology used to determine the representation of each of these documents through their key phrases is explained.
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Keyword:
classification; Computer Sciences; Documents; entity recognition; key phrases; named entities; Physical Sciences and Mathematics; semantic
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URL: https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1107&context=scienmas https://arrow.tudublin.ie/scienmas/107
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