Word Embeddings for Banking Industry (2306.01807v1)

Abstract: Applications of NLP are plentiful, from sentiment analysis to text classification. Practitioners rely on static word embeddings (e.g. Word2Vec or GloVe) or static word representation from contextual models (e.g. BERT or ELMo) to perform many of these NLP tasks. These widely available word embeddings are built from large amount of text, so they are likely to have captured most of the vocabulary in different context. However, how well would they capture domain-specific semantics and word relatedness? This paper explores this idea by creating a bank-specific word embeddings and evaluates them against other sources of word embeddings such as GloVe and BERT. Not surprising that embeddings built from bank-specific corpora does a better job of capturing the bank-specific semantics and word relatedness. This finding suggests that bank-specific word embeddings could be a good stand-alone source or a complement to other widely available embeddings when performing NLP tasks specific to the banking industry.

Domain-Specific Word Embeddings in the Banking Industry

The paper "Word Embeddings for Banking Industry" by Avnish Patel examines the efficacy of creating word embeddings from bank-specific corpora compared to those derived from general corpora. As NLP tasks continue to evolve, practitioners rely on static word embeddings, such as Word2Vec and GloVe, as well as contextual embeddings from models like BERT and ELMo. However, these widely utilized embeddings, constructed from large, generalized text corpora, may not fully capture domain-specific semantics, particularly in specialized fields like banking. This paper's hypothesis contends that word embeddings constructed specifically from banking-related corpora will outperform general embeddings in capturing domain-specific semantics and word relatedness.

Methodology

The paper employs a structured approach to construct bank-specific word embeddings. The bank-specific corpora are sourced from the Consumer Financial Protection Bureau’s (CFPB) complaint database, resulting in a rich dataset of consumer narratives. The method involves generating a co-occurrence matrix from the corpus, followed by dimensionality reduction techniques to create the embeddings. Two primary types of models are evaluated: prediction-based models like Word2Vec and count-based models such as GloVe, alongside pretrained contextual models like BERT.

Various experiments are conducted to ascertain the performance of these embeddings, focusing primarily on intrinsic evaluations. A human-annotated dataset of word-word pairs scored for relatedness serves as the benchmark for evaluating the correlation between predicted and annotated scores. The Spearman correlation coefficient is used to measure the similarity between the embedding-derived scores and human judgment.

Results and Analysis

The results demonstrate that bank-specific word embeddings significantly capture the word semantics and relatedness unique to the banking domain. Among the methodologies examined, two models—namely LSA500 and LSA + Autoencoder—outperform the baseline model based on the Spearman correlation coefficient. These models leverage dimensionality reduction techniques on the co-occurrence matrix derived from domain-specific corpora. Interestingly, pretrained contextual embeddings like those from BERT and other non-banking-specific sources do not perform as well as the bank-specific models.

The analysis extends to examining the top neighbors of key banking terms across different models, clustering performance of highly related word pairs, and the spatial distribution of semantically close terms in embedding space. The findings affirm the hypothesis that embeddings generated from domain-specific texts yield better performance on banking-specific NLP tasks. Particularly, the autoencoder-enhanced latent semantic analysis (LSA) model most accurately clustered related terms and aligned closely with human judgment.

Implications and Future Work

The implications of this research are substantial for NLP applications within the banking industry. The development of bank-specific embeddings offers enhanced accuracy for sentiment analysis, information retrieval, and other domain-centric NLP applications. The positive correlation observed in this paper encourages further exploration into domain-specific embeddings as standalone tools or supplements to general models.

Future research directions suggested include hyperparameter tuning for models using co-occurrence matrix methods, experimenting with alternative weighting techniques like t-tests, and integrating domain-specific embeddings with pretrained models. Expanding the range of input corpora and incorporating newer pretrained models such as GPT-3 could further refine the efficacy of domain-specific embeddings. This paper provides a convincing argument for the value and potential of tailored word embeddings in specialized fields, paving the way for more nuanced NLP solutions.