(Stretch Goal 1) Legislation Network Analysis

[chanteriam]

Build a network graph of references to other legislations and SCOTUS opinions.

[chanteriam]

Key Sources:

• Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
• Doc2Graph GitHub
• Doc2Vec

[chanteriam]

Advice from ChatGPT

To develop a graph linking legislations to each other based on in-text references and textual similarity, you can follow a two-step approach: first, use Natural Language Processing (NLP) and machine learning techniques to estimate the relationships between documents; and second, use graph visualization tools to represent these relationships visually.

Step 1: Estimating the Network

1. In-text References To identify in-text references between legislations, you can use regular expressions or NLP techniques to extract mentions of other documents within each text. This could involve looking for specific patterns that match legal citations or document identifiers.

2. Textual Similarity To estimate textual similarity, you can use various techniques, including:

   TF-IDF (Term Frequency-Inverse Document Frequency): Converts text to a numerical vector representation based on the importance of each term within the document and across the corpus. Scikit-learn's TfidfVectorizer can be used for this.

   Cosine Similarity: Measures the cosine of the angle between two vectors, in this case, TF-IDF vectors, to quantify how similar they are. This can be calculated using scikit-learn's cosine_similarity function.

   Doc2Vec: An extension of the Word2Vec model that learns to represent entire documents as vectors. Gensim's implementation of Doc2Vec can be used to generate document embeddings, with similarity between documents measured by vector distance.

   BERT and Transformer Models: For a more advanced approach, transformer-based models pretrained on a large corpus of text (such as BERT, RoBERTa, etc.) can be used to generate embeddings for each legislation document. The similarity between documents can then be calculated using cosine similarity or other distance metrics on their embeddings. Hugging Face's Transformers library provides easy access to these models.

Step 2: Visualizing the Network

For graph visualization, Python offers several powerful libraries:

NetworkX: A Python library for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It can be used to represent the legislations as nodes and their relationships (references and similarities) as edges with different colors.

Graph-tool: Another Python library for manipulation and statistical analysis of graphs (networks). It's highly efficient but has a steeper learning curve than NetworkX.

Pyvis: A Python library that makes it easy to visualize networks in an interactive manner. Pyvis can be integrated with NetworkX to create interactive visualizations that can be displayed in Jupyter notebooks or saved as HTML files.

Example Workflow

Extract Relationships:

• Use regex/NLP to find references within the text of each document.
• Calculate textual similarity between each pair of documents using your chosen method.

Create the Network:

• Initialize a NetworkX graph.
• Add nodes for each legislation document.
• Add edges for references (one color) and for high textual similarity above a certain threshold (another color).

Visualize:

• Use NetworkX to draw the network, specifying colors for the edges based on the type of relationship, or export the NetworkX graph to Pyvis for an interactive visualization.