Build me a NLP model to extract hidden costs from financial statements

I would like to build a deep learning model using NLP that is able to recognize hidden costs in a 10-K or 10-Q financial statement, and extract the monetary value. There are about 7 different expense categories, each category has different keywords.

Here are some examples:


"Exploratory dry-hole costs were $12.7 million, $1.3 million, and $1.0 million for the years ended December 31, 2012, 2011, and 2010, respectively."

Keyword: "dry-hole costs", "2012"

Output: $12.7 million


"2012 includes the recognition of a $3,340 million impairment charge related to the carrying value of Citi's remaining 35% interest in the Morgan Stanley Smith Barney joint venture"

Keyword: "impairment charge"

Output: $3,340 million


"During the year ended December 31, 2017, we decided to discontinue the internal development of AMG 899, resulting in an impairment charge of $400 million for the IPR&D asset"

Keyword: "impairment charge"

Output: $400 million


"We incurred $146 million of pre-tax expenses in 2017 related to Hurricane Maria."

Keyword: "incurred ... expenses"

Output: $146 million


"In fiscal 2019, we recorded a $53 million charge related to the fair value adjustment of inventory acquired in the Blue Buffalo acquisition."

Keyword: "recorded a ... charge", "fair value adjustment"

Output: $53 million


This is just one category, I have about 100 examples of how they are applied across historic statements that can feed into an initial training set.

There are some problematic sentences that need to be avoided. For example:

"We made $100 million in profit this year, despite having significant restructuring expenses"

The algorithm should realise that although "restructuring expenses" exists in the sentence, the "$100 million" does not refer to it, but to something else and should be ignored.

There are also cases where multiple values are provided in a single sentence, and it needs to pick out the right year:

"Restructuring expenses were $40,000 in 2020, $30,000 in 2019 and $20,000 in 2018"

The correct value here should be $40,000.

I have experimented using spaCy and prodigy, but I am not sure on the best approach. One idea is to develop a NER model that recognizes if a keyword exists in a sentence, and then uses another model to parse the $ value from the sentence, using the year if necessary. It might be better to just use a single training model.

If you need any further details, please reach and out and I can give you more context.

Evner: Datasøgning, Natursprog, Kunstig intelligens, Python, Machine Learning (ML)

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Om arbejdsgiveren:
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Projekt ID: #29836406

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