Meet our team at BioTechX Europe in Basel on the 9-10 October 2024, booth 724. Schedule a meeting with our team HERE.
was successfully added to your cart.

Financial Spanish Sentiment Analysis, German Relation Extraction, ESG Solution Accelerator, Chinese Sentence Embeddings, new demos and much more!

Finance NLP 1.10 comes with a lot of new capabilities added to the 135+ models and 25+ Language Models already available in previous versions of the library. Let’s take a look at each part of NLP for financial services!

Spanish Financial Sentiment Analysis

We have participated in the CodeLab competition for Financial NER and Sentiment Analysis on Spanish texts, training models for the following two tasks:

Target detection. The identification of the economic target of the headline is hindered by the reduced length of the text and the linguistic features of newspaper headlines.

Multi-dimension sentiment classification. As opposed to traditional multi-target tasks in which multiple targets are identified within the scope of each individual processed text, here each news headline refers to a single target entity, but the stances of other economic agents (companies and consumers) are also considered.

As a result, 1 NER model and 3 classifiers (NER target sentiment, consumer sentiment and company sentiment) have been published in our Models Hub repo.

Financial Chinese Sentence Embeddings

Bert and DistilBert Sentence Embeddings provide you with numerical representations (embeddings) of sentences in context. This allows you to calculate the similarity between different financial texts, train classifiers, and cluster your texts. Example of cosine similarity of 8千日利息400元?

Augmented Broker Reports NER

Our Broker Reports model has been augmented with more data.

New demos: Responsibility Reports and QA

In our demo section, you will find three new demos:

Responsibility Reports NER

Understanding Entities in Context: Detecting if an amount is mentioned to be increased or decreased.

Amounts increasing

Amounts decreasing

Financial Question Answering

Financial Table Question Answering.

German NER and Relation Extraction

Detect financial values and financial entities in German Financial Reports.

Also, use Relation Extraction to link those values to their related entities.

Solution Accelerator: Responsibility Reports

A series of notebooks (also known as solution accelerator) have been published here to carry out a series of NLP tasks regarding Responsibility Reports (RR).

  • Text Classification models about ESG (Environment, Social, Governance) with 20+ classes.
  • Named Entity Recognition on RR
  • Table detection and extraction
  • Table Question Answering

Fancy trying?

We’ve got 30-days free licenses for you with technical support from our financial team of technical and SME. This trial includes complete access to more than 135 models, including Classification, NER, Relation Extraction, Similarity Search, Summarization, Sentiment Analysis, Question Answering, etc. and 25+ financial language models.

Just go to https://www.johnsnowlabs.com/install/ and follow the instructions!

Don’t foger to check our notebooks and NLP demo.

How to run

John Snow Lab`s natural language processing for Finance is very easy to run on both clusters and driver-only environments using johnsnowlabs library:

!pip install johnsnowlabs
nlp.install(force_browser=True)
nlp.start()

How useful was this post?

Try Finance NLP

See in action
Our additional expert:
Juan Martinez is a Sr. Data Scientist, working at John Snow Labs since 2021. He graduated from Computer Engineering in 2006, and from that time on, his main focus of activity has been the application of Artificial Intelligence to texts and unstructured data. To better understand the intersection between Language and AI, he complemented his technical background with a Linguistics degree from Moscow Pushkin State Language Institute in 2012 and later on on University of Alcala (2014). He is part of the Healthcare Data Science team at John Snow Labs. His main activities are training and evaluation of Deep Learning, Semantic and Symbolic models within the Healthcare domain, benchmarking, research and team coordination tasks. His other areas of interest are Machine Learning operations and Infrastructure.

Responsibility Reports with Finance NLP, Visual NLP and Table Question Answering

[vc_row type="in_container" full_screen_row_position="middle" column_margin="default" column_direction="default" column_direction_tablet="default" column_direction_phone="default" scene_position="center" text_color="dark" text_align="left" row_border_radius="none" row_border_radius_applies="bg" overflow="visible" overlay_strength="0.3" gradient_direction="left_to_right" shape_divider_position="bottom" bg_image_animation="none"][vc_column column_padding="no-extra-padding" column_padding_tablet="inherit" column_padding_phone="inherit" column_padding_position="all" column_element_direction_desktop="default"...
preloader