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fix: cleanup from live .docx tests (#177)
* add env var for cap threshold; raise default threshold * update docs and tests * added check for ending in a comma * update docs * no caps check for all upper text * capture Text in html and text * check category in Text equality check * lower case all caps before checking for verbs * added check for us city/state/zip * added address type * add address to html * add address to text * fix for text tests; escape for large text segments * refactor regex for readability * update comment * additional test for text with linebreaks * update docs * update changelog * update elements docs * remove old comment * case -> cast * type fix
This commit is contained in:
Matt Robinson
GitHub
parent
1ce8447ba7
commit
339c133326
10
CHANGELOG.md
10
CHANGELOG.md
@ -1,3 +1,13 @@
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## 0.4.5-dev0
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* Loosen the default cap threshold to `0.5`.
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* Add a `NARRATIVE_TEXT_CAP_THRESHOLD` environment variable for controlling the cap ratio threshold.
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* Unknown text elements are identified as `Text` for HTML and plain text documents.
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* `Body Text` styles no longer default to `NarrativeText` for Word documents. The style information
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is insufficient to determine that the text is narrative.
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* Upper cased text is lower cased before checking for verbs. This helps avoid some missed verbs.
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* Adds an `Address` element for capturing elements that only contain an address.
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## 0.4.4
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* Updated `partition_pdf` and `partition_image` to return `unstructured` `Element` objects
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@ -246,8 +246,10 @@ for consideration as narrative text. The function performs the following checks
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* Text that does not contain a verb cannot be narrative text
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* Text that exceeds the specified caps ratio cannot be narrative text. The threshold
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is configurable with the ``cap_threshold`` kwarg. To ignore this check, you can set
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``cap_threshold=1.0``. You may want to ignore this check when dealing with text
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that is all caps.
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``cap_threshold=1.0``. You can also set the threshold by using the
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``NARRATIVE_TEXT_CAP_THRESHOLD`` environment variable. The environment variable
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takes precedence over the kwarg.
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* The cap ratio test does not apply to text that is all uppercase.
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Examples:
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@ -277,8 +279,8 @@ for consideration as a title. The function performs the following checks:
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* Empty text cannot be a title
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* Text that is all numeric cannot be a title
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* If a title contains more than one sentence that exceeds a certain length, it cannot be a title.
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Sentence length threshold is controlled by the ``sentence_min_length`` kwarg and defaults to 5.
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* If a title contains more than one sentence that exceeds a certain length, it cannot be a title. Sentence length threshold is controlled by the ``sentence_min_length`` kwarg and defaults to 5.
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* If a segment of text ends in a comma, it is not considered a potential title. This is to avoid salutations like "To My Dearest Friends," getting flagged as titles.
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Examples:
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@ -320,7 +322,9 @@ Examples:
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Checks if the text contains a verb. This is used in ``is_possible_narrative_text``, but can
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be used independently as well. The function identifies verbs using the NLTK part of speech
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tagger. The following part of speech tags are identified as verbs:
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tagger. Text that is all upper case is lower cased before part of speech detection. This is
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because the upper case letters sometimes cause the part of speech tagger to miss verbs.
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The following part of speech tags are identified as verbs:
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* ``VB``
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* ``VBG``
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@ -374,6 +378,9 @@ Determines if the section of text exceeds the specified caps ratio. Used in
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``is_possible_narrative_text`` and ``is_possible_title``, but can be used independently
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as well. You can set the caps threshold using the ``threshold`` kwarg. The threshold
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defaults to ``0.3``. Only runs on sections of text that are a single sentence.
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You can also set the threshold using the ``NARRATIVE_TEXT_CAP_THRESHOLD`` environment
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variable. The environment variable takes precedence over the kwarg. The caps ratio
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check does not apply to text that is all capitalized.
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Examples:
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@ -11,6 +11,8 @@ elements.
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* ``NarrativeText`` - Sections of a document that include well-formed prose. Sub-class of ``Text``.
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* ``Title`` - Headings and sub-headings wtihin a document. Sub-class of ``Text``.
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* ``ListItem`` - A text element that is part of an ordered or unordered list. Sub-class of ``Text``.
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* ``Address`` - A text item that consists only of an address. Sub-class of ``Text``.
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* ``CheckBox`` - An element representing a check box. Has a ``checked`` element, which is a boolean indicating whether or not that box is checked.
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#########################################
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@ -1,5 +1,7 @@
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This is a test document to use for unit tests.
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Doylestown, PA 18901
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Important points:
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- Hamburgers are delicious
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@ -4,7 +4,7 @@ from lxml import etree
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import pytest
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from unstructured.documents.base import Page
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from unstructured.documents.elements import ListItem, NarrativeText, Title
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from unstructured.documents.elements import Address, ListItem, NarrativeText, Text, Title
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from unstructured.documents.html import (
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LIST_ITEM_TAGS,
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HTMLDocument,
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@ -153,7 +153,7 @@ def test_parse_not_anything(monkeypatch):
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document_tree = etree.fromstring(doc, etree.HTMLParser())
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el = document_tree.find(".//p")
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parsed_el = html._parse_tag(el)
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assert parsed_el is None
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assert parsed_el == Text(text="This is nothing")
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def test_parse_bullets(monkeypatch):
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@ -484,6 +484,7 @@ def test_containers_with_text_are_processed():
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<div dir=3D"ltr">
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<div dir=3D"ltr">Dino the Datasaur<div>Unstructured Technologies<br><div>Data Scientist
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</div>
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<div>Doylestown, PA 18901</div>
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<div><br></div>
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</div>
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</div>
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@ -494,12 +495,13 @@ def test_containers_with_text_are_processed():
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html_document._read()
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assert html_document.elements == [
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Title(text="Hi All,"),
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Text(text="Hi All,"),
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NarrativeText(text="Get excited for our first annual family day!"),
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Title(text="Best."),
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Title(text="Dino the Datasaur"),
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Title(text="Unstructured Technologies"),
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Title(text="Data Scientist"),
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Address(text="Doylestown, PA 18901"),
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]
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@ -4,7 +4,7 @@ import pytest
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import docx
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from unstructured.documents.elements import NarrativeText, Title, Text, ListItem
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from unstructured.documents.elements import Address, NarrativeText, Title, Text, ListItem
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from unstructured.partition.auto import partition
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import unstructured.partition.auto as auto
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@ -115,6 +115,7 @@ def test_auto_partition_html_from_file_rb():
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EXPECTED_TEXT_OUTPUT = [
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NarrativeText(text="This is a test document to use for unit tests."),
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Address(text="Doylestown, PA 18901"),
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Title(text="Important points:"),
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ListItem(text="Hamburgers are delicious"),
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ListItem(text="Dogs are the best"),
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@ -3,7 +3,7 @@ import pytest
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import docx
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from unstructured.documents.elements import ListItem, NarrativeText, Title, Text
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from unstructured.documents.elements import Address, ListItem, NarrativeText, Title, Text
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from unstructured.partition.docx import partition_docx
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@ -14,7 +14,11 @@ def mock_document():
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document.add_paragraph("These are a few of my favorite things:", style="Heading 1")
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# NOTE(robinson) - this should get picked up as a list item due to the •
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document.add_paragraph("• Parrots", style="Normal")
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# NOTE(robinson) - this should get dropped because it's empty
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document.add_paragraph("• ", style="Normal")
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document.add_paragraph("Hockey", style="List Bullet")
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# NOTE(robinson) - this should get dropped because it's empty
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document.add_paragraph("", style="List Bullet")
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# NOTE(robinson) - this should get picked up as a title
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document.add_paragraph("Analysis", style="Normal")
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# NOTE(robinson) - this should get dropped because it is empty
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@ -24,6 +28,8 @@ def mock_document():
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document.add_paragraph("This is my third thought.", style="Body Text")
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# NOTE(robinson) - this should just be regular text
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document.add_paragraph("2023")
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# NOTE(robinson) - this should be an address
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document.add_paragraph("DOYLESTOWN, PA 18901")
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return document
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@ -38,6 +44,7 @@ def expected_elements():
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NarrativeText("This is my first thought. This is my second thought."),
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NarrativeText("This is my third thought."),
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Text("2023"),
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Address("DOYLESTOWN, PA 18901"),
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]
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@ -2,13 +2,14 @@ import os
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import pathlib
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import pytest
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from unstructured.documents.elements import NarrativeText, Title, ListItem
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from unstructured.documents.elements import Address, NarrativeText, Title, ListItem
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from unstructured.partition.text import partition_text
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DIRECTORY = pathlib.Path(__file__).parent.resolve()
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EXPECTED_OUTPUT = [
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NarrativeText(text="This is a test document to use for unit tests."),
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Address(text="Doylestown, PA 18901"),
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Title(text="Important points:"),
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ListItem(text="Hamburgers are delicious"),
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ListItem(text="Dogs are the best"),
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@ -52,3 +53,15 @@ def test_partition_text_raises_with_too_many_specified():
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with pytest.raises(ValueError):
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partition_text(filename=filename, text=text)
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def test_partition_text_captures_everything_even_with_linebreaks():
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text = """
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VERY IMPORTANT MEMO
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DOYLESTOWN, PA 18901
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"""
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elements = partition_text(text=text)
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assert elements == [
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Title(text="VERY IMPORTANT MEMO"),
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Address(text="DOYLESTOWN, PA 18901"),
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]
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@ -1,4 +1,5 @@
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import pytest
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from unittest.mock import patch
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import unstructured.partition.text_type as text_type
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@ -58,6 +59,7 @@ def test_is_possible_narrative_text(text, expected, monkeypatch):
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("7", False), # Fails because it is numeric
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("", False), # Fails because it is empty
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("ITEM 1A. RISK FACTORS", True), # Two "sentences", but both are short
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("To My Dearest Friends,", False), # Ends with a comma
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],
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)
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def test_is_possible_title(text, expected, monkeypatch):
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@ -120,11 +122,10 @@ def test_is_bulletized_text(text, expected):
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[
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("Ask the teacher for an apple", True),
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("Intellectual property", False),
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("THIS MESSAGE WAS APPROVED", True),
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],
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)
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def test_contains_verb(text, expected, monkeypatch):
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monkeypatch.setattr(text_type, "word_tokenize", mock_word_tokenize)
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monkeypatch.setattr(text_type, "pos_tag", mock_pos_tag)
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has_verb = text_type.contains_verb(text)
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assert has_verb is expected
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@ -135,13 +136,26 @@ def test_contains_verb(text, expected, monkeypatch):
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("Intellectual Property in the United States", True),
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("Intellectual property helps incentivize innovation.", False),
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("THIS IS ALL CAPS. BUT IT IS TWO SENTENCES.", False),
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("LOOK AT THIS IT IS CAPS BUT NOT A TITLE.", False),
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("This Has All Caps. It's Weird But Two Sentences", False),
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("The Business Report is expected within 6 hours of closing", False),
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("", False),
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],
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)
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def test_contains_exceeds_cap_ratio(text, expected, monkeypatch):
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assert text_type.exceeds_cap_ratio(text) is expected
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def test_set_caps_ratio_with_environment_variable(monkeypatch):
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monkeypatch.setattr(text_type, "word_tokenize", mock_word_tokenize)
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monkeypatch.setattr(text_type, "sent_tokenize", mock_sent_tokenize)
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assert text_type.exceeds_cap_ratio(text, threshold=0.3) is expected
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monkeypatch.setenv("NARRATIVE_TEXT_CAP_THRESHOLD", 0.8)
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text = "All The King's Horses. And All The King's Men."
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with patch.object(text_type, "exceeds_cap_ratio", return_value=False) as mock_exceeds:
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text_type.is_possible_narrative_text(text)
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mock_exceeds.assert_called_once_with(text, threshold=0.8)
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def test_sentence_count(monkeypatch):
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@ -153,3 +167,19 @@ def test_sentence_count(monkeypatch):
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def test_item_titles():
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text = "ITEM 1(A). THIS IS A TITLE"
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assert text_type.sentence_count(text, 3) < 2
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@pytest.mark.parametrize(
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"text, expected",
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[
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("Doylestown, PA 18901", True),
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("DOYLESTOWN, PENNSYLVANIA, 18901", True),
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("DOYLESTOWN, PENNSYLVANIA 18901", True),
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("Doylestown, Pennsylvania 18901", True),
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(" Doylestown, Pennsylvania 18901", True),
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("The Business Report is expected within 6 hours of closing", False),
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("", False),
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],
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)
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def test_is_us_city_state_zip(text, expected):
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assert text_type.is_us_city_state_zip(text) is expected
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@ -1 +1 @@
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__version__ = "0.4.4" # pragma: no cover
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__version__ = "0.4.5-dev0" # pragma: no cover
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@ -60,7 +60,13 @@ class Text(Element):
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return self.text
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def __eq__(self, other):
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return (self.text == other.text) and (self.coordinates == other.coordinates)
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return all(
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[
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(self.text == other.text),
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(self.coordinates == other.coordinates),
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(self.category == other.category),
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]
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)
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def apply(self, *cleaners: Callable):
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"""Applies a cleaning brick to the text element. The function that's passed in
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@ -108,6 +114,14 @@ class Title(Text):
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pass
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class Address(Text):
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"""A text element for capturing addresses."""
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category = "Address"
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pass
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class Image(Text):
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"""A text element for capturing image metadata."""
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@ -13,12 +13,13 @@ from unstructured.logger import logger
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from unstructured.cleaners.core import clean_bullets, replace_unicode_quotes
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from unstructured.documents.base import Page
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from unstructured.documents.elements import ListItem, Element, NarrativeText, Title
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from unstructured.documents.elements import Address, ListItem, Element, NarrativeText, Text, Title
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from unstructured.documents.xml import XMLDocument
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from unstructured.partition.text_type import (
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is_bulleted_text,
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is_possible_narrative_text,
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is_possible_title,
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is_us_city_state_zip,
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)
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TEXT_TAGS: Final[List[str]] = ["p", "a", "td", "span", "font"]
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@ -47,6 +48,18 @@ class TagsMixin:
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super().__init__(*args, **kwargs)
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class HTMLText(TagsMixin, Text):
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"""Text with tag information."""
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pass
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class HTMLAddress(TagsMixin, Address):
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"""Address with tag information."""
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pass
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class HTMLTitle(TagsMixin, Title):
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"""Title with tag information."""
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@ -203,6 +216,8 @@ def _text_to_element(text: str, tag: str, ancestortags: Tuple[str, ...]) -> Opti
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if not clean_bullets(text):
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return None
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return HTMLListItem(text=clean_bullets(text), tag=tag, ancestortags=ancestortags)
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elif is_us_city_state_zip(text):
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return HTMLAddress(text=text, tag=tag, ancestortags=ancestortags)
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if len(text) < 2:
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return None
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@ -211,8 +226,7 @@ def _text_to_element(text: str, tag: str, ancestortags: Tuple[str, ...]) -> Opti
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elif is_possible_title(text):
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return HTMLTitle(text, tag=tag, ancestortags=ancestortags)
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else:
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# Something that might end up here is text that's just a number.
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return None
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return HTMLText(text, tag=tag, ancestortags=ancestortags)
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def _is_container_with_text(tag_elem: etree.Element) -> bool:
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@ -16,6 +16,23 @@ US_PHONE_NUMBERS_PATTERN = (
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)
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US_PHONE_NUMBERS_RE = re.compile(US_PHONE_NUMBERS_PATTERN)
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# NOTE(robinson) - Based on this regex from regex101. Regex was updated to run fast
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# and avoid catastrophic backtracking
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# ref: https://regex101.com/library/oR3jU1?page=673
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US_CITY_STATE_ZIP_PATTERN = (
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r"(?i)\b(?:[A-Z][a-z.-]{1,15}[ ]?){1,5},\s?"
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r"(?:{Alabama|Alaska|Arizona|Arkansas|California|Colorado|Connecticut|Delaware|Florida"
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r"|Georgia|Hawaii|Idaho|Illinois|Indiana|Iowa|Kansas|Kentucky|Louisiana|Maine|Maryland"
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r"|Massachusetts|Michigan|Minnesota|Mississippi|Missouri|Montana|Nebraska|Nevada|"
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r"New[ ]Hampshire|New[ ]Jersey|New[ ]Mexico|New[ ]York|North[ ]Carolina|North[ ]Dakota"
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r"|Ohio|Oklahoma|Oregon|Pennsylvania|Rhode[ ]Island|South[ ]Carolina|South[ ]Dakota"
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r"|Tennessee|Texas|Utah|Vermont|Virginia|Washington|West[ ]Virginia|Wisconsin|Wyoming}"
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r"|{AL|AK|AS|AZ|AR|CA|CO|CT|DE|DC|FM|FL|GA|GU|HI|ID|IL|IN|IA|KS|KY|LA|ME|MH|MD|MA|MI|MN"
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r"|MS|MO|MT|NE|NV|NH|NJ|NM|NY|NC|ND|MP|OH|OK|OR|PW|PA|PR|RI|SC|SD|TN|TX|UT|VT|VI|VA|"
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r"WA|WV|WI|WY})(, |\s)?(?:\b\d{5}(?:-\d{4})?\b)"
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)
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US_CITY_STATE_ZIP_RE = re.compile(US_CITY_STATE_ZIP_PATTERN)
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UNICODE_BULLETS: Final[List[str]] = [
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"\u0095",
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"\u2022",
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@ -3,20 +3,18 @@ from typing import IO, List, Optional
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import docx
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from unstructured.cleaners.core import clean_bullets
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from unstructured.documents.elements import Element, ListItem, NarrativeText, Text, Title
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from unstructured.documents.elements import Address, Element, ListItem, NarrativeText, Text, Title
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from unstructured.partition.text_type import (
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is_bulleted_text,
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||||
is_possible_narrative_text,
|
||||
is_possible_title,
|
||||
is_us_city_state_zip,
|
||||
)
|
||||
|
||||
# NOTE(robinson) - documentation on built in styles can be found at the link below
|
||||
# ref: https://python-docx.readthedocs.io/en/latest/user/
|
||||
# styles-understanding.html#paragraph-styles-in-default-template
|
||||
STYLE_TO_ELEMENT_MAPPING = {
|
||||
"Body Text": NarrativeText,
|
||||
"Body Text 2": NarrativeText,
|
||||
"Body Text 3": NarrativeText,
|
||||
"Caption": Text, # TODO(robinson) - add caption element type
|
||||
"Heading 1": Title,
|
||||
"Heading 2": Title,
|
||||
@ -87,6 +85,9 @@ def _paragraph_to_element(paragraph: docx.text.paragraph.Paragraph) -> Optional[
|
||||
text = paragraph.text
|
||||
style_name = paragraph.style.name
|
||||
|
||||
if len(text.strip()) == 0:
|
||||
return None
|
||||
|
||||
element_class = STYLE_TO_ELEMENT_MAPPING.get(style_name)
|
||||
|
||||
# NOTE(robinson) - The "Normal" style name will return None since it's in the mapping.
|
||||
@ -100,7 +101,11 @@ def _paragraph_to_element(paragraph: docx.text.paragraph.Paragraph) -> Optional[
|
||||
def _text_to_element(text: str) -> Optional[Text]:
|
||||
"""Converts raw text into an unstructured Text element."""
|
||||
if is_bulleted_text(text):
|
||||
return ListItem(text=clean_bullets(text))
|
||||
clean_text = clean_bullets(text).strip()
|
||||
return ListItem(text=clean_bullets(text)) if clean_text else None
|
||||
|
||||
elif is_us_city_state_zip(text):
|
||||
return Address(text=text)
|
||||
|
||||
if len(text) < 2:
|
||||
return None
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import re
|
||||
from typing import IO, List, Optional
|
||||
|
||||
from unstructured.documents.elements import Element, ListItem, NarrativeText, Title
|
||||
from unstructured.documents.elements import Address, Element, ListItem, NarrativeText, Text, Title
|
||||
|
||||
from unstructured.cleaners.core import clean_bullets
|
||||
from unstructured.nlp.patterns import PARAGRAPH_PATTERN
|
||||
@ -9,6 +9,7 @@ from unstructured.partition.text_type import (
|
||||
is_possible_narrative_text,
|
||||
is_possible_title,
|
||||
is_bulleted_text,
|
||||
is_us_city_state_zip,
|
||||
)
|
||||
|
||||
|
||||
@ -56,11 +57,16 @@ def partition_text(
|
||||
ctext = ctext.strip()
|
||||
|
||||
if ctext == "":
|
||||
break
|
||||
continue
|
||||
if is_bulleted_text(ctext):
|
||||
elements.append(ListItem(text=clean_bullets(ctext)))
|
||||
elif is_us_city_state_zip(ctext):
|
||||
elements.append(Address(text=ctext))
|
||||
elif is_possible_narrative_text(ctext):
|
||||
elements.append(NarrativeText(text=ctext))
|
||||
elif is_possible_title(ctext):
|
||||
elements.append(Title(text=ctext))
|
||||
else:
|
||||
elements.append(Text(text=ctext))
|
||||
|
||||
return elements
|
||||
|
||||
@ -1,15 +1,16 @@
|
||||
"""partition.py implements logic for partitioning plain text documents into sections."""
|
||||
import os
|
||||
import sys
|
||||
|
||||
from typing import List, Optional
|
||||
|
||||
if sys.version_info < (3, 8):
|
||||
from typing_extensions import Final
|
||||
from typing_extensions import Final # pragma: nocover
|
||||
else:
|
||||
from typing import Final
|
||||
|
||||
from unstructured.cleaners.core import remove_punctuation
|
||||
from unstructured.nlp.patterns import US_PHONE_NUMBERS_RE, UNICODE_BULLETS_RE
|
||||
from unstructured.nlp.patterns import US_PHONE_NUMBERS_RE, UNICODE_BULLETS_RE, US_CITY_STATE_ZIP_RE
|
||||
from unstructured.nlp.tokenize import pos_tag, sent_tokenize, word_tokenize
|
||||
from unstructured.logger import logger
|
||||
|
||||
@ -17,8 +18,19 @@ from unstructured.logger import logger
|
||||
POS_VERB_TAGS: Final[List[str]] = ["VB", "VBG", "VBD", "VBN", "VBP", "VBZ"]
|
||||
|
||||
|
||||
def is_possible_narrative_text(text: str, cap_threshold: float = 0.3) -> bool:
|
||||
"""Checks to see if the text passes all of the checks for a narrative text section."""
|
||||
def is_possible_narrative_text(text: str, cap_threshold: float = 0.5) -> bool:
|
||||
"""Checks to see if the text passes all of the checks for a narrative text section.
|
||||
You can change the cap threshold using the cap_threshold kwarg or the
|
||||
NARRATIVE_TEXT_CAP_THRESHOLD environment variable. The environment variable takes
|
||||
precedence over the kwarg.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
text
|
||||
the input text
|
||||
cap_threshold
|
||||
the percentage of capitalized words necessary to disqualify the segment as narrative
|
||||
"""
|
||||
if len(text) == 0:
|
||||
logger.debug("Not narrative. Text is empty.")
|
||||
return False
|
||||
@ -27,6 +39,9 @@ def is_possible_narrative_text(text: str, cap_threshold: float = 0.3) -> bool:
|
||||
logger.debug(f"Not narrative. Text is all numeric:\n\n{text}")
|
||||
return False
|
||||
|
||||
# NOTE(robinson): it gets read in from the environment as a string so we need to
|
||||
# cast it to a float
|
||||
cap_threshold = float(os.environ.get("NARRATIVE_TEXT_CAP_THRESHOLD", cap_threshold))
|
||||
if exceeds_cap_ratio(text, threshold=cap_threshold):
|
||||
logger.debug(f"Not narrative. Text exceeds cap ratio {cap_threshold}:\n\n{text}")
|
||||
return False
|
||||
@ -39,11 +54,23 @@ def is_possible_narrative_text(text: str, cap_threshold: float = 0.3) -> bool:
|
||||
|
||||
|
||||
def is_possible_title(text: str, sentence_min_length: int = 5) -> bool:
|
||||
"""Checks to see if the text passes all of the checks for a valid title."""
|
||||
"""Checks to see if the text passes all of the checks for a valid title.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
text
|
||||
the input text
|
||||
setence_min_length
|
||||
the minimum number of words required to consider a section of text a sentence
|
||||
"""
|
||||
if len(text) == 0:
|
||||
logger.debug("Not a title. Text is empty.")
|
||||
return False
|
||||
|
||||
# NOTE(robinson) - Prevent flagging salutations like "To My Dearest Friends," as titles
|
||||
if text.endswith(","):
|
||||
return False
|
||||
|
||||
if text.isnumeric():
|
||||
logger.debug(f"Not a title. Text is all numeric:\n\n{text}")
|
||||
return False
|
||||
@ -76,6 +103,9 @@ def contains_us_phone_number(text: str) -> bool:
|
||||
def contains_verb(text: str) -> bool:
|
||||
"""Use a POS tagger to check if a segment contains verbs. If the section does not have verbs,
|
||||
that indicates that it is not narrative text."""
|
||||
if text.isupper():
|
||||
text = text.lower()
|
||||
|
||||
pos_tags = pos_tag(text)
|
||||
for _, tag in pos_tags:
|
||||
if tag in POS_VERB_TAGS:
|
||||
@ -109,7 +139,7 @@ def sentence_count(text: str, min_length: Optional[int] = None) -> int:
|
||||
return count
|
||||
|
||||
|
||||
def exceeds_cap_ratio(text: str, threshold: float = 0.3) -> bool:
|
||||
def exceeds_cap_ratio(text: str, threshold: float = 0.5) -> bool:
|
||||
"""Checks the title ratio in a section of text. If a sufficient proportion of the text is
|
||||
capitalized."""
|
||||
# NOTE(robinson) - Currently limiting this to only sections of text with one sentence.
|
||||
@ -118,9 +148,24 @@ def exceeds_cap_ratio(text: str, threshold: float = 0.3) -> bool:
|
||||
logger.debug(f"Text does not contain multiple sentences:\n\n{text}")
|
||||
return False
|
||||
|
||||
if text.isupper():
|
||||
return False
|
||||
|
||||
tokens = word_tokenize(text)
|
||||
if len(tokens) == 0:
|
||||
return False
|
||||
capitalized = sum([word.istitle() or word.isupper() for word in tokens])
|
||||
ratio = capitalized / len(tokens)
|
||||
return ratio > threshold
|
||||
|
||||
|
||||
def is_us_city_state_zip(text) -> bool:
|
||||
"""Checks if the given text is in the format of US city/state/zip code.
|
||||
|
||||
Examples
|
||||
--------
|
||||
Doylestown, PA 18901
|
||||
Doylestown, Pennsylvania, 18901
|
||||
DOYLESTOWN, PENNSYLVANIA 18901
|
||||
"""
|
||||
return US_CITY_STATE_ZIP_RE.match(text.strip()) is not None
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user