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Annoter et prédire des représentations linguistiques de phrases
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In: https://hal.archives-ouvertes.fr/tel-03544267 ; Informatique et langage [cs.CL]. Université de Paris, 2022 (2022)
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Capability Language Processing (CLP): Classification and Ranking of Manufacturing Suppliers Based on Unstructured Capability Data
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Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms
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In: NL4IA Workshop Proceedings ; https://hal.archives-ouvertes.fr/hal-03474696 ; NL4IA Workshop Proceedings, Nov 2021, Milan, Italy (2021)
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About Neural Networks and Writing Definitions
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In: ISSN: 2160-5076 ; Dictionaries: Journal of the Dictionary Society of North America Dictionary Society of North America ; https://hal.archives-ouvertes.fr/hal-03547452 ; Dictionaries: Journal of the Dictionary Society of North America Dictionary Society of North America, 2021, 42 (2), ⟨10.1353/dic.2021.0022⟩ (2021)
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Models of diachronic semantic change using word embeddings ; Modèles diachroniques à base de plongements de mot pour l'analyse du changement sémantique
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In: https://tel.archives-ouvertes.fr/tel-03199801 ; Document and Text Processing. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG006⟩ (2021)
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Transdisciplinary Analysis of a Corpus of French Newsreels: The ANTRACT Project
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In: ISSN: 1938-4122 ; Digital Humanities Quarterly ; https://hal.archives-ouvertes.fr/hal-03166755 ; Digital Humanities Quarterly, Alliance of Digital Humanities, 2021, Special Issue on AudioVisual Data in DH, 15 (1) ; http://digitalhumanities.org/dhq/ (2021)
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TRUNAJOD: A text complexity library to enhance natural language processing ...
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TRUNAJOD: A text complexity library to enhance natural language processing ...
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HeadlineStanceChecker: Exploiting summarization to detect headline disinformation
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Dataset for Temporal Analysis of English-French Cognates
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In: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020) ; 12th Conference on Language Resources and Evaluation (LREC 2020) ; https://hal.archives-ouvertes.fr/hal-03026957 ; 12th Conference on Language Resources and Evaluation (LREC 2020), May 2020, Marseille, France. pp.855-859, ⟨10.5281/zenodo.3693650⟩ (2020)
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Towards the Prediction of Semantic Complexity Based on Concept Graphs ; Vers la prédiction de complexité sémantique fondée sur les graphes de concepts
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In: 12th International Conference on Educational Data Mining (EDM 2019) ; https://hal.archives-ouvertes.fr/hal-02315319 ; 12th International Conference on Educational Data Mining (EDM 2019), Jul 2019, Montreal, Canada. pp.188-197 ; educationaldatamining.org/EDM2019/ (2019)
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| 13 |
Towards a DRS Parsing Framework for French
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In: Proceedings Advances in Natural Language Processing ; SNAMS 2019 : Sixth International Conference on Social Networks Analysis, Management and Security ; https://hal.archives-ouvertes.fr/hal-02280800 ; SNAMS 2019 : Sixth International Conference on Social Networks Analysis, Management and Security, Oct 2019, Granada, Spain. ⟨10.1109/SNAMS.2019.8931868⟩ (2019)
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A Multilingual Semantic Similarity-Based Approach for Question-Answering Systems
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In: International Conference on Knowledge Science, Engineering and Management KSEM 2019 ; https://hal.archives-ouvertes.fr/hal-02465511 ; International Conference on Knowledge Science, Engineering and Management KSEM 2019, Aug 2019, Athènes, Greece. ⟨10.1007/978-3-030-29551-6_54⟩ (2019)
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| 15 |
Ligt: An LLOD-Native Vocabulary for Representing Interlinear Glossed Text as RDF
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Ionov, Maxim; Chiarcos, Christian. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2019. : OASIcs - OpenAccess Series in Informatics. 2nd Conference on Language, Data and Knowledge (LDK 2019), 2019
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Semantic Network Analysis Pipeline—Interactive Text Mining Framework for Exploration of Semantic Flows in Large Corpus of Text
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In: Applied Sciences ; Volume 9 ; Issue 24 (2019)
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Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems
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BAYOMI, MOSTAFA MOHAMED. - : Trinity College Dublin. School of Computer Science & Statistics. Discipline of Computer Science, 2019
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Abstract:
APPROVED ; The volume of digital content resources written as text documents is growing every day, at an unprecedented rate. Because this content is generally not structured as easy-to-handle units, it can be very difficult for users to find information they are interested in, or to help them accomplish their tasks. This in turn has increased the need for producing tailored content that can be adapted to the needs of individual users. A key challenge for producing such tailored content lies in the ability to understand how this content is structured. Hence, the efficient analysis and understanding of unstructured text content has become increasingly important. This has led to the increasing use of Natural Language Processing (NLP) techniques to help with processing unstructured text documents. Amongst the different NLP techniques, Text Segmentation is specifically used to understand the structure of textual documents. However, current approaches to text segmentation are typically based upon using lexical and/or syntactic representation to build a structure from the unstructured text documents. However, the relationship between segments may be semantic, rather than lexical or syntactic. Furthermore, text segmentation research has primarily focused on techniques that can be used to process text documents but not on how these techniques can be utilised to produce tailored content that can be adapted to the needs of individual users. In contrast, the field of Adaptive Systems has inherently focused on the challenges associated with dynamically adapting and delivering content to individual users. However, adaptive systems have primarily focused upon the techniques of adapting content, not on how to understand and structure this content. Even systems that have focused on structuring content are limited in that they rely upon the original structure of the content resource, which reflects the perspective of its author. Therefore, these systems are limited in that they do not deeply ?understand? the structure of the content, which in turn, limits their capability to discover and supply appropriate content for use in defined contexts, and limits the content?s amenability for reuse within various independent adaptive systems. In order to utilise the strength of NLP techniques to overcome the challenges of understanding unstructured text content, this thesis investigates how NLP techniques can be utilised in order to enhance the supply of content to adaptive systems. Specifically, the contribution of this thesis is concerned with addressing the challenges associated with hierarchical text segmentation techniques, and with content discoverability and reusability for adaptive systems. Firstly, this research proposes a novel hierarchical text segmentation approach, named C-HTS, that builds a structure from text documents based on the semantic representation of text. Semantic representation is a method that replaces keyword-based text representation with concept-based features, where the meaning of a piece of text is represented as a vector of knowledge concepts automatically extracted from massive human knowledge repositories such as Wikipedia. Using this approach, C-HTS represents the content of a document as a tree-like hierarchy. This way of structuring the document can be regarded as a hierarchically coherent tree that is useful for supporting a variety of search methods as it provides different levels of granularity for the underlying content. Secondly, this research proposes a novel content-supply service named CROCC. The aim of CROCC is to utilise the produced structure of C-HTS in order to overcome the limitations of the state of the art content-supply approaches. Finally, this research conducts an evaluation of the extent to which the CROCC service enhances content discoverability and reusability for adaptive systems.
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Keyword:
Adaptive Systems; Natural Language Processing; Semantic Analysis; Text Segmentation
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URL: https://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:BAYOMIM
http://hdl.handle.net/2262/86062
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| 18 |
Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems
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BAYOMI, MOSTAFA. - : Trinity College Dublin. School of Computer Science & Statistics. Discipline of Computer Science, 2019
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| 20 |
Extraction d'interactions entre aliment et médicament : Etat de l'art et premiers résultats
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In: Rencontres des Jeunes Chercheur-euse-s ; https://hal.archives-ouvertes.fr/hal-01836499 ; Rencontres des Jeunes Chercheur-euse-s, May 2018, Rennes, France (2018)
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