Mainak Ghosh, M.Sc.

Doktorand und wissenschaftlicher Mitarbeiter

Innovation and Entrepreneurship Research

+49 89 24246-574
mainak.ghosh(at)ip.mpg.de

Persönliche Website:

https://ghoshmainak.github.io

Arbeitsbereiche:

Natürliche Spracherkennung und -verarbeitung, Innovationsökonomik, Science of Science, Immaterialgüterrechte

Wissenschaftlicher Werdegang

Seit 03/2020
Wissenschaftlicher Mitarbeiter und Doktorand, Max-Planck-Institut für Innovation und Wettbewerb (Innovation and Entrepreneurship Research)

10/2017 - 11/2019
Master of Science (M.Sc.) im Studiengang “Data Engineering and Analytics”, Technische Universität München (TUM); Titel der Masterarbeit: “Multilingual Opinion Mining on Social Media Comments Using Unsupervised Neural Clustering Methods”

11/2017 - 02/2018
Studentischer Mitarbeiter, Max-Planck-Institut für Sozialrecht und Sozialpolitik, München

05/2013 - 07/2013
Forschungspraktikum, Indian Statistical Institute, Kalkutta, Indien

06/2012 - 07/2012
Praktikum, Globsyn Business School, Kalkutta, Indien

2010 - 2014
Bachelor of Engineering (B.E.) im Studiengang “Computer Science & Technology”, Indian Institute of Engineering Science & Technology, Shibpur, Indien

Beruflicher Werdegang

03/2018 - 03/2020
Werkstudent, IDS GmbH – Analysis and Reporting Services (IDS), München

08/2014 - 09/2017
Softwareingenieur, Acclaris Business Solutions Pvt Ltd, Kalkutta, Indien

Wissenschaftliche Preise und Ehrungen

2013
Zertifikat CCS (Cognizant Certified Student), IT Foundation Skills

2009
Auszeichnung, Mathematischer Kompetenztest, Association for Improvement of Mathematics Teaching (AIMT), Kalkutta, Indien

2008
Zertifikat für besondere Leistungen in Physikwissenschaften und Mechanik

Publikationen

Artikel in referierten Fachzeitschriften

Chatterjee, Chirantan; Chugunova, Marina; Ghosh, Mainak; Singhal, Abhay; Wang, Lucy Xiaolu (2023). Human Mediation Leads to Higher Compliance in Digital Mental Health: Field Evidence from India, Frontiers in Behavioral Economics, 2. DOI

    Konferenzbeiträge

    Hagerer, Gerhard; Kirchhoff, Martin; Danner, Hannah; Pesch, Robert; Ghosh, Mainak; Roy, Archishman; Jiaxi, Zhao; Groh, Georg (2021). SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining, in: Galia Angelova et al. (Hg.), Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021) - Deep Learning for Natural Language Processing Methods and Applications, INCOMA Ltd., Shoumen 2021, 475-482.

    • Recent research in opinion mining proposedword embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a graph with topics as nodes and their correlations as edges. Further details are displayed interactively to support the exploration of large text collections, e.g., representative words and sentences of topics, topic and sentiment distributions, hierarchical topic clustering, and customizable, predefined topic labels. The toolkit optimizes automatically on custom data for optimal coherence. We show a working instance of the toolkit on data crawled from English social media discussions about organic food consumption. The visualization confirms findings of a qualitative consumer research study. SocialVisTUM and its training procedures are accessible online.
    • Event: International Conference "Recent Advances in Natural Language Processing, Shoumen, 2021-09-01

    Andere Veröffentlichungen, Presseartikel, Interviews

    Hagerer, Gerhard; Moeed, Abdul; Dugar, Sumit; Gupta, Sarthak; Ghosh, Mainak; Danner, Hannah; Mitevski, Oliver; Nawroth, Andreas; Groh, Georg (2020). An Evaluation of Progressive Neural Networks for Transfer Learning in Natural Language Processing, in: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), Marseille, 1376-1381.

    • A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.
    • Conference Paper
    • Conference Volume
    • Event: 12th Language Resources and Evaluation Conference, Marseille, 2020-05-11

    Diskussionspapiere

    Erhardt, Sebastian; Ghosh, Mainak; Buunk, Erik; Rose, Michael; Harhoff, Dietmar (2022). Logic Mill - A Knowledge Navigation System, arXiv preprint 2301.00200.

    • Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.
    • https://doi.org/10.48550/arXiv.2301.00200

    Vorträge

    01.12.2021
    Logic Mill: Patent Embedding
    Forschungsseminar
    Ort: Schloss Ringberg


    01.10.2021
    Logic Mill: Automation of Patent Full-Text Collection
    Forschungsseminar
    Ort: Feldkirchen-Westerham


    23.03.2021
    Logic Mill - Citation Prediction
    Forschungsseminar
    Ort: online


    23.03.2021
    Automation and Mental Health Platform Design: Field Experiment Plan
    Forschungsseminar
    Ort: online


    10.09.2020
    Knowledge Mining, Digitalization, Machine Learning
    Forschungsseminar
    Ort: online (München)