## 2 Literature Review
### NLP Alignment Evaluation  
This subsection focuses on works that surveyed or compared the performance of NLP and ML methods. [PERSON] and [PERSON] (2019) presented a comprehensive survey of topic modeling, highlighting challenges in their quantitative evaluation [28]. [PERSON] et al. (2023) provided a recent survey categorizing topic modeling techniques into algebraic, fuzzy, probabilistic, and neural categories [29]. They reviewed the diversity and evolution of topic models and found that the research trends are moving toward developing and tuning neural topic models such as LLMs. [PERSON] et al. (2023) focused on methods for assessing the quality of topic models. They explored metrics such as reconstruction error, topic coherence, and stability analysis, emphasizing that different metrics capture various aspects of model fit [30]. Their findings suggested that a combination of indices, coupled with human validation, is essential for assessing the performance of topic models, a perspective that highlights the complexities of evaluating model quality. [PERSON] et al. (2021) empirically evaluated the challenges in systematically comparing topic modeling algorithms, revealing shortcomings in common practices and highlighting the need for a standardized approach in model benchmarking [31]. [PERSON] and [PERSON] (2020) reviewed various topic modeling methods, focusing on their ability to handle complex data relationships, such as correlations between topics and topic changes over time. Their work encouraged diversity in the choice of topic modeling methods, particularly for complex datasets [32]. [PERSON] et al. (2023) compared human and artificial semantic representations in topic modeling [3]. They demonstrated that NLP embeddings still fall short of human-like semantic representations. [PERSON] et al. (2022) compared non-application-specific topic modeling algorithms and assessed their performance against known clustering [33]. They found that metrics used so far provided a mixed picture that made it difficult to verify the accuracy of topic modeling outputs, concluding that topic model evaluation remains an unresolved issue [33]. [PERSON] et al. (2021) questioned the validity of automated topic model evaluation, suggesting a disparity between automated coherence metrics and human judgment [34].  
The above studies demonstrate the wide range of applications for NLP and ML in patent analysis, highlighting the complexities in evaluating algorithmic interpretations in topic modeling and the importance of human validation. Relative to these studies, the present study stands out by uniquely combining topic modeling techniques with SME expertise to quantify alignment in patent topic classification, particularly in the context of CV patents. The proposed topic alignment index, combined with an optimized NLP pipeline, fills a crucial gap in the literature by offering a more rigorous and complete approach in establishing a performance benchmark to contrast with future advancements. As NLP and ML techniques continue to advance, their role in patent analysis is likely to expand, providing researchers, practitioners, and policymakers with even more powerful tools for understanding, managing, and forecasting technological innovation. Hence, this study not only contributes to the existing body of knowledge, but also opens new avenues for future research in patent analysis.