Quarterly Mark December 2023 - 29

Asuncion, A., Welling, M., Smola, A. J., & Teh, Y. W. (2009). On smoothing
and inference for topic models. Proceedings of the 26th Annual International
Conference on Machine Learning.
Note: These are just a few examples of influential papers on lexicon-based
models and topic modeling in NLP and there are many other papers that have
made significant contributions to these areas.
authors: Can you give me overview of the word embeddings based methods in
NLP and their use in finance?
chatgpt: Word embeddings are a type of NLP technique that represent words
as continuous-valued vectors in a high-dimensional space. These word vectors
capture semantic and syntactic information about words in a way that can be
used for various NLP tasks, including sentiment analysis, text classification, and
language translation.
In finance, word embeddings have been used for tasks such as:
Sentiment Analysis: Word embeddings have been used to analyze the
sentiment of financial news articles, investor reports, and social media posts, in
order to gauge market sentiment and predict stock prices.
Text Classification: Word embeddings have been used to classify financial
documents into categories such as earnings reports, balance sheets, and press
releases.
Risk Assessment: Word embeddings have been used to analyze the risk
associated with financial products, by identifying potential red flags in
regulatory reports and other financial documents.
Topic Modeling: Word embeddings have been used in topic modeling of
financial documents, in order to identify important themes and topics in
financial data.
Overall, word embeddings have proven to be a powerful tool for NLP in
finance, as they enable the use of high-level abstractions to analyze large
amounts of unstructured financial data, and provide insights into market
sentiment and risk.
authors: Can you give me citations for the above methods in BibTeX format?
chatgpt: I'm sorry, I cannot provide citations for all the word embedding
methods listed in my previous response. However, here are some papers that
cover popular word embedding methods: ...
authors: What are the disadvantages of embedding based methods in NLP?
chatgpt: Some disadvantages of word embedding based methods in NLP
include:
Data sensitivity: word embeddings are highly dependent on the training
corpus, and can be sensitive to the type of data and domain they are trained
on.
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Quarterly Mark December 2023

Table of Contents for the Digital Edition of Quarterly Mark December 2023

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Quarterly Mark December 2023 - Back Cover
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