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Jun 09, 2020

Antoine Louis



Obtaining accurate information about products in a fast and efficient way is becoming increasingly important at Cisco as the related documentation rapidly grows. Thanks to recent progress in natural language processing (NLP), extracting valuable information from domain-general documents has gained in popularity, and deep learning has boosted the development of effective text mining systems. However, directly applying the advancements in NLP to domain-specific documentation might yield unsatisfactory results due to a word distribution shift from domain-general language to domain-specific language. Hence, this thesis aims to determine if a large language model pre-trained on domain-specific (computer networking) text corpora improves performance over the same model pre-trained exclusively on general domain text, when evaluated on in-domain text mining tasks.

To this end, we introduce NetBERT (Bidirectional Encoder Representations from Transformers for Computer Networking), a domain-specific language representation model based on BERT and pre-trained on large-scale computer networking corpora. Through several extrinsic and intrinsic evaluations, we compare the performance of our novel model against the domain-general BERT. We demonstrate clear improvements over BERT on the following two representative text mining tasks: networking text classification (0.9% F1 improvement) and networking information retrieval (12.3% improvement on a custom retrieval score). Additional experiments on word similarity and word analogy tend to show that NetBERT capture more meaningful semantic properties and relations between networking concepts than BERT does. We conclude that pre-training BERT on computer networking corpora helps it understand more accurately domain-related text.


For attribution in academic contexts, please cite this work as:

    title={NetBERT: A Pre-trained Language Representation Model for Computer Networking},
    author={Louis, Antoine},
    school={University of Liege}