Quarterly Mark December 2023 - 24

rules and grammars to analyze and understand natural language text. Later,
statistical NLP models emerged in the 1980s and 1990s, which used machine
learning algorithms to learn patterns in text data and make predictions about
the meaning of text.
authors: When were the advanced statistical NLP models developed? Can you
give me a timeline?
chatgpt: A timeline of the development of advanced statistical NLP models is as
follows:
Late 1990s to early 2000s: The first statistical NLP models, such as hidden
Markov models (HMMs) and maximum entropy models, were developed and
applied to various NLP tasks.
Mid 2000s to early 2010s: The widespread availability of large text corpora and
advancements in machine learning algorithms led to the development of more
sophisticated statistical NLP models, such as support vector machines (SVMs)
and deep neural networks (DNNs).
Late 2010s to present: With the advent of big data and improvements in
computational resources, deep learning techniques, such as recurrent neural
networks (RNNs) and transformer models, have become the dominant NLP
models and have been applied to a wide range of NLP tasks, including finance.
authors: I have heard about lexicon-based models and topic models. When
were they developed and they belong to which category of the NLP models?
chatgpt: " Lexicon-based " models and " topic models " are both categories of
statistical NLP models.
Lexicon-based models: These models were developed in the early 2000s and
rely on the use of predefined dictionaries or lexicons of words and phrases to
analyze and understand natural language text. For example, a lexicon-based
model might use a dictionary of financial terms to identify financial concepts in
news articles.
Topic models: These models were developed in the late 1990s and early
2000s and use statistical techniques to identify the main topics discussed in
a document or a collection of documents. For example, a topic model might
identify market trends or company news as topics in financial news articles.
Both lexicon-based models and topic models are considered to be early forms
of statistical NLP models and have been largely superseded by more advanced
deep learning techniques, such as RNNs and transformers. However, they still
play an important role in some NLP applications, particularly in the early stages
of preprocessing and feature extraction.
authors: Can you give me a detailed timeline of the development of NLP models
and their use in finance?
chatgpt: A detailed timeline of the development of NLP models and their use in
finance is as follows:
24 | From ELIZA to ChatGPT: The Evolution of Natural Language Processing and Financial Applications

Quarterly Mark December 2023

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

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Quarterly Mark December 2023 - Inside Front Cover
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Quarterly Mark December 2023 - Back Cover
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