eyecite is an open source tool for extracting legal citations from text. It is used, among other things, to annotate millions of legal documents in the collections of CourtListener and the Caselaw Access Project.

eyecite recognizes a wide variety of citations commonly appearing in American legal decisions, including:

• full case: Bush v. Gore, 531 U.S. 98, 99-100 (2000)
• short case: 531 U.S., at 99
• statutory: Mass. Gen. Laws ch. 1, § 2
• law journal: 1 Minn. L. Rev. 1
• supra: Bush, supra, at 100
• id.: Id., at 101

You can see all of the citation patterns eyecite looks for over in reporters_db.

All contributors, corrections, and additions are welcome!

This project is the culmination of years of work to build a citator within CourtListener. This project represents the next step in that development: decoupling the parsing logic and exposing it for third-party use as a standalone Python package.

Since eyecite was factored out from the CourtListener codebase into a standalone package, it has been developed in collaboration with the Caselaw Access Project.

Install eyecite:

pip install eyecite

Here's a short example of extracting citations and their metadata from text:

from eyecite import get_citations

text = """
    Mass. Gen. Laws ch. 1, § 2 (West 1999) (barring ...).
    Foo v. Bar, 1 U.S. 2, 3-4 (1999) (overruling ...).
    Id. at 3.
    Foo, supra, at 5.
"""

get_citations(text)

# returns:
[
    FullLawCitation(
        'Mass. Gen. Laws ch. 1, § 2',
        groups={'reporter': 'Mass. Gen. Laws', 'chapter': '1', 'section': '2'},
        metadata=Metadata(parenthetical='barring ...', pin_cite=None, year='1999', publisher='West', ...)
    ),
    FullCaseCitation(
        '1 U.S. 2',
        groups={'volume': '1', 'reporter': 'U.S.', 'page': '2'},
        metadata=Metadata(parenthetical='overruling ...', pin_cite='3-4', year='1999', court='scotus', plaintiff='Foo', defendant='Bar,', ...)
    ),
    IdCitation(
        'Id.',
        metadata=Metadata(pin_cite='at 3')
    ),
    SupraCitation(
        'supra,',
        metadata=Metadata(antecedent_guess='Foo', pin_cite='at 5', ...)
    )
]

Here's a full-featured example of efficiently extracting citations from an HTML document and annotating them with links to documents in a database.

First our imports:

# imports
from eyecite import get_citations, clean_text, resolve_citations, annotate
from eyecite.models import FullCaseCitation
from eyecite.resolve import resolve_full_citation
from eyecite.tokenizers import HyperscanTokenizer

We want to insert links into a piece of HTML like this:

text = """
    <p>1 <i>U.S.</i> 2, 1 S.Ct. 2.<p>
    <p>Id.</p>
    <p>Mass. Gen.    Laws ch. 1, § 2.</p>
"""

Note that tags may overlap with citations and whitespace may be uneven. We want "1 U.S. 2", "1 S.Ct. 2" and "Id." to all link to the same URL fetched from our database, since they all refer to the same case. Any cites we don't have in our database will link to "/unknown_cite"

First we'll get the text ready for cite extraction:

cleaned_text = clean_text(text, ['html', 'all_whitespace'])

# cleaned_text:
# "1 U.S. 2, 1 S.Ct. 2. Id. Mass. Gen. Laws ch. 1, § 2."

Next we'll extract citations using a custom tokenizer. Unlike the default tokenizer this uses hyperscan for much faster extraction, with a precompiled regular expression database stored in .test_cache/. This depends on "pip install hyperscan":

tokenizer = HyperscanTokenizer(cache_dir=".test_cache")
citations = get_citations(cleaned_text, tokenizer=tokenizer)

# citations:
# [
#   FullCaseCitation('1 U.S. 2'),
#   FullCaseCitation('1 S.Ct. 2'),
#   IdCitation(),
#   FullLawCitation('Mass. Gen. Laws ch. 1, § 2'),
# ]

Now we want to resolve all of the extracted cites into clusters indexed by the resource they refer to, such as a case or statute. We'll use a custom function to resolve a given full cite to its resource, so we can return our own MyCaseModel for citations we recognize. We'll fall back on returning resolve_full_citation() for citations we don't recognize.

For this simplified example, we'll assume we have a database model MyCaseModel so that MyCaseModel.get_by_citation() will return the case referred to by that citation string. In real life this might be a Django model or Elasticsearch lookup. We'll also assume that the same case has the parallel citations "1 U.S. 2" and "1 S. Ct. 2", so MyCaseModel.get_by_citation("1 U.S. 2") returns the same case as MyCaseModel.get_by_citation("1 S. Ct. 2").

def resolve_cite(cite):
    if isinstance(cite, FullCaseCitation):
        resource = MyCaseModel.get_by_citation(cite.corrected_citation())
        if resource:
            return resource
    return resolve_full_citation(cite)

resolutions = resolve_citations(citations, resolve_full_citation=resolve_cite)

# resolutions:
# {
#   MyCaseModel('1 U.S. 2'): [FullCaseCitation('1 U.S. 2'), FullCaseCitation('1 S.Ct. 2'), IdCitation()],
#   eyecite.models.Resource(...): [FullLawCitation('Mass. Gen. Laws ch. 1, § 2')],
# }

(Note the use of cite.corrected_citation(), which returns "1 S. Ct. 2" for the matched citation "1 S.Ct. 2". reporters_db includes many variations for reporter names, so it's useful to match cases by their corrected reporters rather than the exact string found in the text.)

Finally we can prepare annotations for each citation in our clusters. An annotation is text to insert back into cleaned_text, like ((<start offset>, <end offset>), <before text>, <after text>):

annotations = []
for resource, cites in resolutions.items():
    if isinstance(resource, MyCaseModel):
        # add link to case we were able to resolve:
        url = resource.frontend_url
    else:
        # add link to case we weren't able to resolve:
        url = f"/unknown_cite?cite={resource.citation.matched_text()}"
    for cite in cites:
        annotations.append((cite.span(), f"<a href='{url}'>", f"</a>"))

Now we have annotations ready to add to clean_text, but we actually want to insert them into our original text variable with HTML formatting. We can pass source_text=text into annotate() to have the annotation positions adjusted and inserted into text using the diff-match-patch library:

annotated_text = annotate(cleaned_text, annotations, source_text=text)

# annotated_text:
# """
#     <p><a href='/us/1/2/'>1 <i>U.S.</i> 2</a>, <a href='/us/1/2/'>1 S.Ct. 2</a>.<p>
#     <p><a href='/us/1/2/'>Id.</a></p>
#     <p><a href='/unknown_cite?cite=Mass. Gen. Laws ch. 1, § 2'>Mass. Gen.    Laws ch. 1, § 2</a>.</p>
# """

Ta da!

get_citations(), the main executable function, takes several parameters.

1. remove_ambiguous ==> bool, default False: whether to remove citations that might refer to more than one reporter and can't be narrowed down by date.
2. tokenizer ==> Tokenizer, default eyecite.tokenizers.default_tokenizer: an instance of a Tokenizer object (see "Tokenizers" below)

For a given citation text such as "... 1 Baldwin's Rep. 1 ...", eyecite expects that the text will be "clean" before being passed to get_citation. This means:

• Spaces will be single space characters, not multiple spaces or other whitespace.
• Quotes and hyphens will be standard quote and hyphen characters.
• No junk such as HTML tags inside the citation.

You can use clean_text to help with this:

from eyecite import clean_text, get_citations

source_text = '<p>foo   1  U.S.  1   </p>'
plain_text = clean_text(text, ['html', 'inline_whitespace', my_func])
found_citations = get_citations(plain_text)

See the Annotating Citations section for how to insert links into the original text using citations extracted from the cleaned text.

clean_text currently accepts these values as cleaners:

1. inline_whitespace: replace all runs of tab and space characters with a single space character
2. all_whitespace: replace all runs of any whitespace character with a single space character
3. underscores: remove two or more underscores, a common error in text extracted from PDFs
4. html: remove non-visible HTML content using the lxml library
5. Custom function: any function taking a string and returning a string.

For simple plain text, you can insert links to citations using the annotate function:

from eyecite import get_citations, annotate

plain_text = 'bob lissner v. test 1 U.S. 12, 347-348 (4th Cir. 1982)'
citations = get_citations(plain_text)
linked_text = annotate(plain_text, [[c.span(), "<a>", "</a>"] for c in citations])

returns:
'bob lissner v. test <a>1 U.S. 12</a>, 347-348 (4th Cir. 1982)'

Each citation returned by get_citations keeps track of where it was found in the source text. As a result, annotate must be called with the same cleaned text used by get_citations to extract citations. If you do not, the offsets returned by the citation's span method will not align with the text, and your annotations will be in the wrong place.

If you want to clean text and then insert annotations into the original text, you can pass the original text in as source_text:

from eyecite import get_citations, annotate, clean_text

source_text = '<p>bob lissner v. <i>test   1 U.S.</i> 12,   347-348 (4th Cir. 1982)</p>'
plain_text = clean_text(source_text, ['html', 'inline_whitespace'])
citations = get_citations(plain_text)
linked_text = annotate(plain_text, [[c.span(), "<a>", "</a>"] for c in citations], source_text=source_text)

returns:
'<p>bob lissner v. <i>test   <a>1 U.S.</i> 12</a>,   347-348 (4th Cir. 1982)</p>'

The above example extracts citations from plain_text and applies them to source_text, using a diffing algorithm to insert annotations in the correct locations in the original text.

Note that the above example includes mismatched HTML tags: "<a>1 U.S.</i> 12</a>". To specify handling for unbalanced tags, use the unbalanced_tags parameter:

• unbalanced_tags="skip": annotations that would result in unbalanced tags will not be inserted.
• unbalanced_tags="wrap": unbalanced tags will be wrapped, resulting in <a>1 U.S.</a></i><a> 12</a>

Important: unbalanced_tags="wrap" uses a simple regular expression and will only work for HTML where angle brackets are properly escaped, such as the HTML emitted by lxml.html.tostring. It is intended for regularly formatted documents such as case text published by courts. It may have unpredictable results for deliberately-constructed challenging inputs such as citations containing partial HTML comments or <pre> tags.

If inserting text before and after isn't sufficient, supply a callable under the annotator parameter that takes (before, span_text, after) and returns the annotated text:

def annotator(before, span_text, after):
    return before + span_text.lower() + after
linked_text = annotate(plain_text, [[c.span(), "<a>", "</a>"] for c in citations], annotator=annotator)

returns:
'bob lissner v. test <a>1 u.s. 12</a>, 347-348 (4th Cir. 1982)'

Once you have extracted citations from a document, you may wish to resolve them to their common references. To do so, just pass the results of get_citations() into resolve_citations(). This function will do its best to resolve each "full," "short form," "supra," and "id" citation to a common Resource object, returning a dictionary that maps resources to lists of associated citations:

from eyecite import get_citations, resolve_citations

text = 'first citation: 1 U.S. 12. second citation: 2 F.3d 2. third citation: Id.'
found_citations = get_citations(text)
resolved_citations = resolve_citations(found_citations)

returns (pseudo):
{
    <Resource object>: [FullCaseCitation('1 U.S. 12')],
    <Resource object>: [FullCaseCitation('2 F.3d 2'), IdCitation('Id.')]
}

Importantly, eyecite performs these resolutions using only its immanent knowledge about each citation's textual representation. If you want to perform more sophisticated resolution (e.g., by augmenting each citation with information from a third-party API), simply pass custom resolve_id_citation(), resolve_supra_citation(), resolve_shortcase_citation(), and resolve_full_citation() functions to resolve_citations() as keyword arguments. You can also configure those functions to return a more complex resource object (such as a Django model), so long as that object inherits the eyecite.models.ResourceType type (which simply requires hashability). For example, you might implement a custom full citation resolution function as follows, using the default resolution logic as a fallback:

def my_resolve(full_cite):
    # special handling for resolution of known cases in our database
    resource = MyOpinion.objects.get(full_cite)
    if resource:
        return resource
    # allow normal clustering of other citations
    return resolve_full_citation(full_cite)

resolve_citations(citations, resolve_full_citation=my_resolve)

returns (pseudo):
{
    <MyOpinion object>: [<full_cite>, <short_cite>, <id_cite>],
    <Resource object>: [<full cite>, <short cite>],
}

If you want to see what metadata eyecite is able to extract for each citation, you can use dump_citations. This is primarily useful for developing eyecite, but may also be useful for exploring what data is available to you:

In [1]: from eyecite import dump_citations, get_citations

In [2]: text="Mass. Gen. Laws ch. 1, § 2. Foo v. Bar, 1 U.S. 2, 3-4 (1999). Id. at 3. Foo, supra, at 5."

In [3]: cites=get_citations(text)

In [4]: print(dump_citations(get_citations(text), text))
FullLawCitation: Mass. Gen. Laws ch. 1, § 2. Foo v. Bar, 1 U.S. 2, 3-4 (1
  * groups
    * reporter='Mass. Gen. Laws'
    * chapter='1'
    * section='2'
FullCaseCitation: Laws ch. 1, § 2. Foo v. Bar, 1 U.S. 2, 3-4 (1999). Id. at 3. Foo, s
  * groups
    * volume='1'
    * reporter='U.S.'
    * page='2'
  * metadata
    * pin_cite='3-4'
    * year='1999'
    * court='scotus'
    * plaintiff='Foo'
    * defendant='Bar,'
  * year=1999
IdCitation: v. Bar, 1 U.S. 2, 3-4 (1999). Id. at 3. Foo, supra, at 5.
  * metadata
    * pin_cite='at 3'
SupraCitation: 2, 3-4 (1999). Id. at 3. Foo, supra, at 5.
  * metadata
    * antecedent_guess='Foo'
    * pin_cite='at 5'

In the real terminal, the span() of each extracted citation will be highlighted. You can use the context_chars=30 parameter to control how much text is shown before and after.

Internally, eyecite works by applying a list of regular expressions to the source text to convert it to a list of tokens:

In [1]: from eyecite.tokenizers import default_tokenizer

In [2]: list(default_tokenizer.tokenize("Foo v. Bar, 123 U.S. 456 (2016). Id. at 457."))
Out[2]:
['Foo',
 StopWordToken(data='v.', ...),
 'Bar,',
 CitationToken(data='123 U.S. 456', volume='123', reporter='U.S.', page='456', ...),
 '(2016).',
 IdToken(data='Id.', ...),
 'at',
 '457.']

Tokens are then scanned to determine values like the citation year or case name for citation resolution.

Alternate tokenizers can be substituted by providing a tokenizer instance to get_citations():

from eyecite.tokenizers import HyperscanTokenizer
hyperscan_tokenizer = HyperscanTokenizer(cache_dir='.hyperscan')
cites = get_citations(text, tokenizer=hyperscan_tokenizer)

test_FindTest.py includes a simplified example of using a custom tokenizer that uses modified regular expressions to extract citations with OCR errors.

eyecite ships with two tokenizers:

The default tokenizer uses the pyahocorasick library to filter down eyecite's list of extractor regexes. It then performs extraction using the builtin re library.

The alternate HyperscanTokenizer compiles all extraction regexes into a hyperscan database so they can be extracted in a single pass. This is far faster than the default tokenizer (exactly how much faster depends on how many citation formats are included in the target text), but requires the optional hyperscan dependency that is limited to the x86 platform.

Compiling the hyperscan database takes several seconds, so short-running scripts may want to provide a cache directory where the database can be stored. The directory should be writeable only by the user:

hyperscan_tokenizer = HyperscanTokenizer(cache_dir='.hyperscan')

Installing eyecite is easy.

poetry add eyecite

Or via pip:

sh
pip install eyecite

Or install the latest dev version from github

sh
pip install git+https://github.com/freelawproject/eyecite.git@master

1. Update version info in pyproject.toml.

For an automated deployment, tag the commit with vx.y.z, and push it to master. An automated deploy will be completed for you.

For a manual deployment, run:

sh
poetry publish --build

eyecite comes with a robust test suite of different citation strings that it is equipped to handle. Run these tests as follows:

python3 -m unittest discover -s tests -p 'test_*.py'

If you would like to create mock citation objects to assist you in writing your own local tests, import and use the following functions for convenience:

from eyecite.test_factories import (
    case_citation,
    id_citation,
    nonopinion_citation,
    supra_citation,
)

This repository is available under the permissive BSD license, making it easy and safe to incorporate in your own libraries.

Pull and feature requests welcome. Online editing in GitHub is possible (and easy!).