  Research in scientific disciplines evolves, often rapidly, over time with the
emergence of novel methodologies and their associated terminologies. While
methodologies themselves being conceptual in nature and rather difficult to
automatically extract and characterise, in this paper, we seek to develop
supervised models for automatic extraction of the names of the various
constituents of a methodology, e.g., `R-CNN', `ELMo' etc. The main research
challenge for this task is effectively modeling the contexts around these
methodology component names in a few-shot or even a zero-shot setting. The main
contributions of this paper towards effectively identifying new evolving
scientific methodology names are as follows: i) we propose a factored approach
to sequence modeling, which leverages a broad-level category information of
methodology domains, e.g., `NLP', `RL' etc.; ii) to demonstrate the feasibility
of our proposed approach of identifying methodology component names under a
practical setting of fast evolving AI literature, we conduct experiments
following a simulated chronological setup (newer methodologies not seen during
the training process); iii) our experiments demonstrate that the factored
approach outperforms state-of-the-art baselines by margins of up to 9.257\% for
the methodology extraction task with the few-shot setup.

Information Retrieval;Digital Libraries;Machine Learning