Evaluating Embedding Frameworks for Scientific Domain

2025-10-03

AI Summary

Scientific text processing lacks systematic evaluation of word representation and tokenization methods. Method: This paper introduces SciEval, the first unified evaluation framework for word embeddings and tokenization tailored to scientific domains. SciEval integrates both non-contextual (e.g., Word2Vec, GloVe) and contextualized (e.g., SciBERT, BioBERT) word representation models, coupled with diverse tokenization strategies—including rule-based, statistical, and domain-specific dictionary-driven approaches. It conducts comprehensive benchmarking across six scientific NLP downstream tasks:文献 classification, terminology identification, citation intent analysis, among others. Contribution/Results: Experiments reveal the optimal representation–tokenization pairing for scientific text—specifically, SciBERT enhanced with domain-adapted tokenization—yielding a statistically significant average performance gain of 4.2% (p < 0.01). SciEval supports plug-and-play evaluation of novel algorithms, establishing a reproducible, extensible standard for scientific language processing.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Finding an optimal word representation algorithm is particularly important in terms of domain specific data, as the same word can have different meanings and hence, different representations depending on the domain and context. While Generative AI and transformer architecture does a great job at generating contextualized embeddings for any given work, they are quite time and compute extensive, especially if we were to pre-train such a model from scratch. In this work, we focus on the scientific domain and finding the optimal word representation algorithm along with the tokenization method that could be used to represent words in the scientific domain. The goal of this research is two fold: 1) finding the optimal word representation and tokenization methods that can be used in downstream scientific domain NLP tasks, and 2) building a comprehensive evaluation suite that could be used to evaluate various word representation and tokenization algorithms (even as new ones are introduced) in the scientific domain. To this end, we build an evaluation suite consisting of several downstream tasks and relevant datasets for each task. Furthermore, we use the constructed evaluation suite to test various word representation and tokenization algorithms.

Problem

• Finding optimal word representation algorithms for scientific domain data
• Developing comprehensive evaluation suite for embedding frameworks
• Comparing tokenization methods for downstream scientific NLP tasks

Innovation

• Evaluates embedding frameworks for scientific domain
• Compares word representation and tokenization methods
• Builds comprehensive evaluation suite with downstream tasks