Mohamed - Amine  Boukhaled

Ph.D in Computer Science, LIP6, Sorbonne University UPMC Paris-6
Former Researcher at Lattice Lab, CNRS (Langues, Textes, Traitements informatiques, Cognition Lab)
Former Assistant Professor (ATER) at Engineering Department of Sorbonne University UPMC Paris-6
mohamed.boukhaled [at] gmail [dot] com     Linkedin Profile


PhD Thesis

Title: On Computational Stylistics: Mining Literary Texts for the Extraction of Characterizing Stylistic Patterns.

Discipline: Computer Science - Option: Computational Stylistics and Natural Language Processing.
Date awarded: Septembre 2016.
Institution: Paris-6 University - France.
Grade: First class honours (Très honorable).
Keywords: computational stylistics, sequential data mining, knowledge discovery, text mining, morpho-syntactic pattern, outlier detection, computational authorship study.
Details: The thesis locates itself in the interdisciplinary field of computational stylistics, namely the application of statistical and computational methods to the study of literary style. As main contribution, we worked on an approach to the computational stylistic study of classic French literary texts based on a hermeneutic point of view, in which discovering interesting linguistic patterns (morphosyntactic patterns) is done without any prior knowledge. We proposed a knowledge discovery process for the stylistic characterization with an emphasis on the syntactic dimension of style by extracting relevant patterns from a given text. The analyzed results of the experimental evaluation indicate that the presented techniques are fairly effective in extracting interesting syntactic patterns, especially if we take into account the unsupervised nature of this process. This seems particularly promising as a computer-assisted literary analysis tool to support linguists and literary researchers in their stylistic analysis.

Boukhaled M.A., 2016. On Computational Stylistics: Mining Literary Texts for the Extraction of Characterizing Stylistic Patterns. Thése de doctorat, sous la direction du Prof. Jean-Gabriel Ganascia, Université UPMC Paris-6.

Master Degree 

Discipline: Computer Science - Option: Artificial Intelligence and the web.
Date awarded: Juin 2013.
Institution: Joseph Fourier- Grenoble 1 University - France.
Keywords: automatic classification, ambiguity, natural language parser, parse tree.
Details: In my master thesis I proposed an unsupervised automatic classification method of the ambiguities produced by a natural language parser.

Boukhaled M.A., 2016. Classification automatique des ambiguïtés produites par un analyseur de la langue naturelle, sous la direction du Prof. Hervé Blanchon et Prof.Eric Gaussier, Université Joseph Fourier- Grenoble 1.

Engineering Degree 

Discipline: Computer Science - Option: Information Systems.
Date awarded: Juin 2012.
Institution: Higher School of Computer Science - Algeria.
Keywords: Key Performance Indicator, prospective dashboard.
Details: My engineering dissertation was about the design and the implementation of a prospective dashboard for the Higher School of Computer Science at Algiers.

Boukhaled M.A., 2012. Conception et réalisation d'un tableau de bord prospectif pour l'ESI, sous la direction du Prof. Abdessamed Réda Ghomari , Ecole Nationale Supérieure d’Informatique. 

High School Diploma

- Scientific Baccalaureate, Algiers, Algeria - 2007.


  • Head of IA and Big Data Masters / Lead Instructor at ITESCIA (September 2019 ~ now).
  • Teaching and Research Assistant (ATER) in Computer Science, Paris-6 University (September 2016 - August 2017).
  • IT tutor, Paris-Sorbonne University (Sept. 2013 - Jan 2014).
Teaching Content:

Teaching Duties at ITESCIA School:

  • NoSQL Databases: MongoDB 
  • Big Data Analysis with Spark 
  • Python for Data Analysis 
  • Java Advanced Programming 
  • IA for Game development 
  • Artificiel Intelligence: Concepts and Techniques 
  • Machine Learning: Introductory Course 
  • Deep Learning: Advanced Course 
Teaching Duties at the Engineering Department of Paris-6 UMPC Sorbonne University:

  • NoSQL Databases: MongoDB 
  • Elements of Programming 1i001 
  • Objects Oriented Programming, 2i002 
  • Initiation to Databases, 2I009 
  • Initiation to Databases, 2I009 
  • Objects Programming, 3i002 
  • Artificial Intelligence 
  • Digital Humanities 


International Journals: 

Frontini, F., Boukhaled, M.A. & Ganascia, J.-G. (2017). Mining for characterising patterns in literature using correspondence analysis: an experiment on French novels. Digital Humanities Quarterly 11 (2)
Boukhaled, M.A., Frontini, F., Bourgne, G., & Ganascia, J.-G. (2015). Computational Study of Stylistics: a Clustering-based Interestingness Measure for Extracting Relevant Syntactic Patterns. International Journal of Computational Linguistics and Applications 6 (1): 45–62.

Book Chapters: 

Boukhaled, M.A., Fagard B., Poibeau T. (2019) The Dynamics of Semantic Change: A Corpus-Based Analysis. Agents and Artificial Intelligence. Lecture Notes in Computer Science, vol 11978. Springer, Cham
Frontini, F., Boukhaled, M.A. & Ganascia, J.-G. (2018). Approaching French Theatrical Characters by Syntactical Analysis: A Study with Motifs and Correspondence Analysis.”In Grammar of Genres and Styles. From Discrete to Non-Discrete Units. Trends in Linguistics. De Gruyter Mouton. (pp. 118–39).
Boukhaled, M.A. & Ganascia, J.-G. (2017). Stylistic Features Based on Sequential Rule Mining for Authorship Attribution. In Cognitive Approach to Natural Language Processing. Elsevier. (pp. 159-175).

Conferences and Workshops with proceedings: 

Boukhaled, M.A., Fagard, B. and Poibeau, T., (2019). Modelling the Semantic Change Dynamics using Diachronic Word Embedding. In 11th International Conference on Agents and Artificial Intelligenc, ICAART 2019. Prague, Czech Republic.
Boukhaled, M.A., Sellami, Z., & Ganascia, J.-G. (2015). Phoebus: un Logiciel d’Extraction de Réutilisations dans des Textes Littéraires. In 22ème Conférence sur le Traitement Automatique des Langues Naturelles. Caen, France.
Oudni, A., Boukhaled, M.A., & Bourgne, G. (2015). Analyse des relations et des dynamiques de corpus de textes littéraires par extraction de motifs graduels. In 24ème Conférence sur la Logique Floue et ses Applications, LFA2015. Poitiers, France.
Boukhaled, M.A., Frontini, F., & Ganascia, J.-G. (2015). Une mesure d’intérêt à base de surreprésentation pour l'extraction des motifs syntaxiques stylistiques. In 22ème Conférence sur le Traitement Automatique des Langues Naturelles. Caen, France.
Boukhaled, M.A., Frontini, F., & Ganascia, J.-G. (2015). A Peculiarity-based Exploration of Syntactical Patterns: a Computational Study of Stylistics. In Workshop on Interactions between Data Mining and Natural Language Processing DMNLP’15 ECML/PKDD 2015 Workshop (pp. 31–40). Porto, Portugal.
Boukhaled, M.A., & Ganascia, J.-G. (2014). Using Function Words for Authorship Attribution: Bag-Of-Words vs. Sequential Rules. In The 11th International Workshop on Natural Language Processing and Cognitive Science (pp. 115–122). Venice, Italy: DE GRUYTER.
Boukhaled, M.A., & Ganascia, J.-G. (2014). Probabilistic Anomaly Detection Method for Authorship Verification. In S. I. Publishing (Ed.), 2nd International Conference on Statistical Language and Speech Processing, SLSP 2014, Springer in the LNCS/LNAI Series (Vol. 8791, pp. 211–219). Grenoble, France.

Conferences, Workshops & Talks without proceedings: 

Boukhaled, M.A. (2015). Une méthode non supervisée pour la vérification d’auteur à base d'un modèle gaussien multivarié. In 10es Rencontres Jeunes Chercheurs en Recherche d’Information (RJCRI) (pp. 525–533). Paris, France: ARIA.
Frontini, F., Boukhaled, M.A., & Ganascia, J.-G. (2015). Moliere’s Raisonneurs: a quantitative study of distinctive linguistic patterns. Corpus Linguistics 2015, Lancaster, UK.
Riguet, M., Jolivet, V., & Boukhaled, M.A. (2015). «Cohérence sémantique»: l’apport des algorithmes de représentation vectorielle des mots. In 8es Journées Internationales de Linguistique de Corpus. Orleans, France.


Academic activities:
  • Program committee member (Poster and demo Chairs) of the 14th ACS/IEEE International Conference on Computer Systems and Applications AICCSA 2017
  • EReMoS: EReMoS (Extraction et REcherche de MOtifs Syntaxiques -Syntactic Pattern Extraction and Matching) is a computational stylistics tool developed by the ACASA team at the computer science laboratory of Paris 6 (LIP6). A syntactic pattern is a syntagmatic sequence constituted by a set of syntactic elements. This tool offers not only the possibility to extract such patterns from a loaded text, but also to search and browse the textual instances of those patterns.
Training certificates:
  • Deep Laerning Specialization certificate - Coursera /
  • Certificate from UCREL Summer School in Corpus-based NLP
  • Statement of accomplishment of the coursera’s “Machine Learning” course signed by Prof. Andrew Ng
  • Statement of accomplishment of the coursera’s “Mining Massive Datasets” course
  • Microsoft Certified Technology Specialist, MCTS : SQL Server 2008 database development
Technical skills:
  • Programming Languages (Python, Java)
  • Databases (SQL, NoSQL, XML)
  • Excellent Command of Machine & Deep Learning (Scikit-learn, Keras, TensoFlow)
  • Big Data Framework (Spark)
  • NLP and Text Mining Frameworks (NLTK, SpaCy)
  • Data Mining & Data Analysis Libraries (Pandas, Numpy, Matplotlib )
  • Versioning System (Git)
  • Fluent in the following languages: Arabic, French and English.