diff --git a/coh_metrix/cohesion.py b/coh_metrix/cohesion.py
index 382869edf26446e4538fd052168c2d0348c7decf..02de6db17b3d0c71efeaaa9214eddf5c48279fe4 100644
--- a/coh_metrix/cohesion.py
+++ b/coh_metrix/cohesion.py
@@ -1,13 +1,3 @@
-"""
-This module calculates cohesion metrics for a given text.
-The metrics are divided into adjacent and global cohesion, where global
-cohesion is only calculated if the amount of sentences is below the threshold
-MAX_SENTENCES, as defined in coh_metrics/configuration.py.
-
-These metrics need to be verified in full, especially anaphor overlap.
-Furthermore, training a new model to include transformer embeddings as well as
-coreference resolution would be beneficial.
-"""
import numpy as np
from spacy.tokens import Token, Doc
@@ -15,252 +5,229 @@ from coh_metrix import configuration as config
def calculate_overlaps(token1: Token, token2: Token, main: dict, other: dict):
- """
- A helper function to minimize code duplication in the cohesion calculation.
- Can be used for both adjacent and global cohesion.
- :param token1: the first token.
- :param token2: the second token.
- :param main: the main dictionary to update.
- :param other: the other dictionary to update.
- """
-
- # Noun overlap
- if (
- token1.text.lower() == token2.text.lower()
- and token1.pos_ == token2.pos_
- and token1.pos_ in ["NOUN", "PROPN"]
- ):
- main["nouns"] += 1
-
- # Argument overlap
- if (
- token1.text == token2.text and token1.pos_ == "PRON" and token2.pos_ == "PRON"
- ) or (
- token1.lemma_ == token2.lemma_
- and token1.pos_ == "NOUN"
- and token2.pos_ == "NOUN"
- ):
- main["arguments"] += 1
-
- # Stem overlap
- if (
- token1.lemma_ == token2.lemma_
- and token1.pos_ in ["NOUN", "VERB", "ADJ", "ADV"]
- and token2.pos_ == "NOUN"
- ):
- main["stems"] += 1
-
- # Content word overlap
-
- if token1.lemma_ == token2.lemma_:
- if token1.pos_ == "PRON" and token2.pos_ == "PRON":
- other["pronouns"]["bin"] += 1
- elif token1.pos_ == "NOUN" and token2.pos_ == "NOUN":
- other["nouns"]["bin"] += 1
- elif token1.pos_ == "ADV" and token2.pos_ == "ADV":
- other["adverbs"]["bin"] += 1
- elif token1.pos_ == "ADJ" and token2.pos_ == "ADJ":
- other["adjectives"]["bin"] += 1
- elif token1.pos_ == "VERB" and token2.pos_ == "VERB":
- other["verbs"]["bin"] += 1
+ """
+ A helper function to minimize code duplication in the cohesion calculation.
+ Can be used for both adjacent and global cohesion.
+ :param token1: the first token.
+ :param token2: the second token.
+ :param main: the main dictionary to update.
+ :param other: the other dictionary to update.
+ """
+
+ # Noun overlap
+ if (
+ token1.text.lower() == token2.text.lower()
+ and token1.pos_ == token2.pos_
+ and token1.pos_ in ["NOUN", "PROPN"]
+ ):
+ main["nouns"] += 1
+
+ # Argument overlap
+ if (
+ token1.text == token2.text and token1.pos_ == "PRON" and token2.pos_ == "PRON"
+ ) or (
+ token1.lemma_ == token2.lemma_
+ and token1.pos_ == "NOUN"
+ and token2.pos_ == "NOUN"
+ ):
+ main["arguments"] += 1
+
+ # Stem overlap
+ if (
+ token1.lemma_ == token2.lemma_
+ and token1.pos_ in ["NOUN", "VERB", "ADJ", "ADV"]
+ and token2.pos_ == "NOUN"
+ ):
+ main["stems"] += 1
+
+ # Content word overlap (for other bins)
+ if token1.lemma_ == token2.lemma_:
+ if token1.pos_ == "PRON" and token2.pos_ == "PRON":
+ other["pronouns"]["bin"] += 1
+ elif token1.pos_ == "NOUN" and token2.pos_ == "NOUN":
+ other["nouns"]["bin"] += 1
+ elif token1.pos_ == "ADV" and token2.pos_ == "ADV":
+ other["adverbs"]["bin"] += 1
+ elif token1.pos_ == "ADJ" and token2.pos_ == "ADJ":
+ other["adjectives"]["bin"] += 1
+ elif token1.pos_ == "VERB" and token2.pos_ == "VERB":
+ other["verbs"]["bin"] += 1
def gen_num(np_morph, pron_morph):
- """
- Helper function to determine gender and number agreement between a noun phrase
- and a pronoun.
- :param np_morph: the morphological features of the noun phrase.
- :param pron_morph: the morphological features of the pronoun.
- :return: True if the noun phrase and pronoun agree.
- """
- try:
- genus_np = next(iter(np_morph.get("Gender")))
- numerus_np = next(iter(np_morph.get("Number")))
- genus_pron = next(iter(pron_morph.get("Gender")))
- numerus_pron = next(iter(pron_morph.get("Number")))
- except StopIteration:
- return False
-
- if genus_np == genus_pron and numerus_np == numerus_pron:
- return True
+ """
+ Helper function to determine gender and number agreement between a noun phrase
+ and a pronoun.
+ :param np_morph: the morphological features of the noun phrase.
+ :param pron_morph: the morphological features of the pronoun.
+ :return: True if the noun phrase and pronoun agree.
+ """
+ try:
+ genus_np = next(iter(np_morph.get("Gender")))
+ numerus_np = next(iter(np_morph.get("Number")))
+ genus_pron = next(iter(pron_morph.get("Gender")))
+ numerus_pron = next(iter(pron_morph.get("Number")))
+ except StopIteration:
return False
+ return (genus_np == genus_pron and numerus_np == numerus_pron)
+
def run_cohesion(doc: Doc) -> dict:
- """
- Runs the cohesion metrics. Adjacent cohesion is always calculated, whereas global
- cohesion is only calculated if the amount of sentences is below the threshold
- MAX_PARAGRAPHS, as defined in coh_metrics/configuration.py.
- :param doc: the spaCy doc object containing the parsed text.
- :return: the calculated cohesion metrics.
- """
- sentences = list(doc.sents)
- content_tags = {"NOUN", "VERB", "ADJ", "ADV"}
- noun_tags = {"NOUN", "PROPN"}
-
- _adjacent = {
- "nouns": 0,
- "arguments": 0,
- "stems": 0,
- "content_words": {"ratio": 0, "std": 0},
- "anaphors": 0,
- }
-
- _other_adjacent = {
- "nouns": {"bin": 0, "avg_ratio": 0},
- "adjectives": {"bin": 0, "avg_ratio": 0},
- "adverbs": {"bin": 0, "avg_ratio": 0},
- "verbs": {"bin": 0, "avg_ratio": 0},
- "pronouns": {"bin": 0, "avg_ratio": 0},
- }
-
- _global = {
- "nouns": 0,
- "arguments": 0,
- "stems": 0,
- "content_words": {"ratio": 0, "std": 0},
- "anaphors": 0,
- }
-
- _other_global = {
- "nouns": {"bin": 0, "avg_ratio": 0},
- "adjectives": {"bin": 0, "avg_ratio": 0},
- "adverbs": {"bin": 0, "avg_ratio": 0},
- "verbs": {"bin": 0, "avg_ratio": 0},
- "pronouns": {"bin": 0, "avg_ratio": 0},
- }
-
- adjacent_total_pairs = 0
- global_total_pairs = 0
-
- content_word_overlap_adjacent = []
- content_word_overlap_global = []
-
- doc_length = len(sentences)
-
- max_sentences_reached = False
-
- for i in range(doc_length - 1):
- sentence1 = sentences[i]
- sentence2 = sentences[i + 1]
-
- adjacent_pairs = len(sentence1) * len(sentence2)
-
- content_word_pairs = 0
- content_word_overlaps = 0
-
- # Calculate adjacent metrics
- for token1 in sentence1._.cohesion_tokens:
- for token2 in sentence2._.cohesion_tokens:
- calculate_overlaps(token1, token2, _adjacent, _other_adjacent)
-
- # Content word overlap
- if token1.pos_ in content_tags and token2.pos_ in content_tags:
- content_word_pairs += 1
- if token1.lemma_ == token2.lemma_:
- _adjacent["content_words"]["ratio"] += 1
- content_word_overlaps += 1
-
- # Anaphor overlap
- for token in sentence2:
- if token.pos_ == "PRON":
- for np_ in sentence1._.np_chunks:
- if gen_num(np_["morph"], token.morph):
- _adjacent["anaphors"] += 1
-
- # Normalize the other adjacent categories
- for cat in _other_adjacent.keys():
- if _other_adjacent[cat]["bin"] > 0:
- _other_adjacent[cat]["avg_ratio"] += (
- _other_adjacent[cat]["bin"] / adjacent_pairs
- )
-
- adjacent_total_pairs += 1
-
- content_word_overlap_adjacent.append(
- content_word_overlaps / content_word_pairs
- if content_word_pairs > 0 and content_word_overlaps > 0
- else 0
- )
-
- # only allow global metrics if the document is not too long
- if doc_length >= config.MAX_SENTENCES:
- max_sentences_reached = True
- continue
-
- # Calculate global metrics
- for j in range(i + 1, len(sentences)):
- sentence2_global = sentences[j]
-
- global_pairs = len(sentence1) * len(sentence2)
-
- content_word_pairs = 0
- content_word_overlaps = 0
-
- for token1 in sentence1._.cohesion_tokens:
- for token2 in sentence2_global._.cohesion_tokens:
- calculate_overlaps(token1, token2, _global, _other_global)
-
- # Content word overlap
- if token1.pos_ in content_tags and token2.pos_ in content_tags:
- content_word_pairs += 1
- if token1.lemma_ == token2.lemma_:
- _global["content_words"]["ratio"] += 1
- content_word_overlaps += 1
-
- # Anaphor overlap
- # TODO: Global anaphor overlap considers past X sentences
- for token in sentence2_global:
- if token.pos_ == "PRON":
- for np_ in sentence1._.np_chunks:
- if gen_num(np_["morph"], token.morph):
- _global["anaphors"] += 1
-
- # Normalize the other global categories
- for cat in _other_global.keys():
- if _other_global[cat]["bin"] > 0:
- _other_global[cat]["avg_ratio"] += (
- _other_global[cat]["bin"] / global_pairs
- )
-
- global_total_pairs += 1
-
- content_word_overlap_global.append(
- content_word_overlaps / content_word_pairs
- if content_word_pairs > 0 and content_word_overlaps > 0
- else 0
- )
-
- for cat in _adjacent.keys():
- if cat == "content_words":
- _adjacent["content_words"]["std"] = (
- np.std(np.array(content_word_overlap_adjacent))
- if adjacent_total_pairs > 0
- else "NaN"
- )
- _adjacent["content_words"]["ratio"] /= adjacent_total_pairs
- else:
- _adjacent[cat] /= adjacent_total_pairs
-
- _adjacent["other"] = _other_adjacent
-
- if max_sentences_reached:
- return {"adjacent": _adjacent, "global": None}
-
- for cat in _global.keys():
- if cat == "content_words":
- _global["content_words"]["std"] = (
- np.std(np.array(content_word_overlap_global))
- if global_total_pairs > 0
- else "NaN"
- )
- _global["content_words"]["ratio"] /= global_total_pairs
- else:
- _global[cat] /= global_total_pairs
-
- _global["other"] = _other_global
-
- return {
- "adjacent": _adjacent,
- "global": _global,
- }
+ """
+ Runs the cohesion metrics. Adjacent cohesion is always calculated, whereas global
+ cohesion is only calculated if the amount of sentences is below the threshold
+ MAX_PARAGRAPHS, as defined in coh_metrics/configuration.py.
+ :param doc: the spaCy doc object containing the parsed text.
+ :return: the calculated cohesion metrics.
+ """
+ sentences = list(doc.sents)
+ content_tags = {"NOUN", "VERB", "ADJ", "ADV"}
+
+ _adjacent = {
+ "nouns": 0,
+ "arguments": 0,
+ "stems": 0,
+ "content_words": {"ratio": 0, "std": 0},
+ "anaphors": 0,
+ }
+ _other_adjacent = {
+ "nouns": {"bin": 0, "avg_ratio": 0},
+ "adjectives": {"bin": 0, "avg_ratio": 0},
+ "adverbs": {"bin": 0, "avg_ratio": 0},
+ "verbs": {"bin": 0, "avg_ratio": 0},
+ "pronouns": {"bin": 0, "avg_ratio": 0},
+ }
+
+ _global = {
+ "nouns": 0,
+ "arguments": 0,
+ "stems": 0,
+ "content_words": {"ratio": 0, "std": 0},
+ "anaphors": 0,
+ }
+ _other_global = {
+ "nouns": {"bin": 0, "avg_ratio": 0},
+ "adjectives": {"bin": 0, "avg_ratio": 0},
+ "adverbs": {"bin": 0, "avg_ratio": 0},
+ "verbs": {"bin": 0, "avg_ratio": 0},
+ "pronouns": {"bin": 0, "avg_ratio": 0},
+ }
+
+ adjacent_total_pairs = 0
+ global_total_pairs = 0
+ content_word_overlap_adjacent = []
+ content_word_overlap_global = []
+
+ doc_length = len(sentences)
+ max_sentences_reached = False
+
+ for i in range(doc_length - 1):
+ sentence1 = sentences[i]
+ sentence2 = sentences[i + 1]
+ adjacent_pairs = len(sentence1) * len(sentence2)
+
+ # capture starting bin counts for per-pair calculation
+ start_bins = {cat: _other_adjacent[cat]["bin"] for cat in _other_adjacent}
+
+ content_word_pairs = 0
+ content_word_overlaps = 0
+
+ # Calculate overlaps for this adjacent pair
+ for token1 in sentence1._.cohesion_tokens:
+ for token2 in sentence2._.cohesion_tokens:
+ calculate_overlaps(token1, token2, _adjacent, _other_adjacent)
+ if token1.pos_ in content_tags and token2.pos_ in content_tags:
+ content_word_pairs += 1
+ if token1.lemma_ == token2.lemma_:
+ _adjacent["content_words"]["ratio"] += 1
+ content_word_overlaps += 1
+
+ # Anaphor overlap
+ for token in sentence2:
+ if token.pos_ == "PRON":
+ for np_ in sentence1._.np_chunks:
+ if gen_num(np_["morph"], token.morph):
+ _adjacent["anaphors"] += 1
+
+ # update per-pair avg_ratio using local bin increments
+ for cat in _other_adjacent:
+ local_count = _other_adjacent[cat]["bin"] - start_bins[cat]
+ if local_count > 0:
+ _other_adjacent[cat]["avg_ratio"] += (local_count / adjacent_pairs)
+
+ adjacent_total_pairs += 1
+ content_word_overlap_adjacent.append(
+ content_word_overlaps / content_word_pairs
+ if content_word_pairs > 0 and content_word_overlaps > 0
+ else 0
+ )
+
+ # only allow global metrics if the document is not too long
+ if doc_length >= config.MAX_SENTENCES:
+ max_sentences_reached = True
+ continue
+
+ # Calculate global metrics
+ for j in range(i + 1, doc_length):
+ sentence2_global = sentences[j]
+ global_pairs = len(sentence1) * len(sentence2_global)
+
+ start_bins_g = {cat: _other_global[cat]["bin"] for cat in _other_global}
+ cw_pairs_g = 0
+ cw_overlaps_g = 0
+
+ for token1 in sentence1._.cohesion_tokens:
+ for token2 in sentence2_global._.cohesion_tokens:
+ calculate_overlaps(token1, token2, _global, _other_global)
+ if token1.pos_ in content_tags and token2.pos_ in content_tags:
+ cw_pairs_g += 1
+ if token1.lemma_ == token2.lemma_:
+ _global["content_words"]["ratio"] += 1
+ cw_overlaps_g += 1
+
+ for token in sentence2_global:
+ if token.pos_ == "PRON":
+ for np_ in sentence1._.np_chunks:
+ if gen_num(np_["morph"], token.morph):
+ _global["anaphors"] += 1
+
+ for cat in _other_global:
+ local_count_g = _other_global[cat]["bin"] - start_bins_g[cat]
+ if local_count_g > 0:
+ _other_global[cat]["avg_ratio"] += (local_count_g / global_pairs)
+
+ global_total_pairs += 1
+ content_word_overlap_global.append(
+ cw_overlaps_g / cw_pairs_g if cw_pairs_g > 0 and cw_overlaps_g > 0 else 0
+ )
+
+ # finalize adjacent metrics
+ if adjacent_total_pairs > 0:
+ _adjacent["content_words"]["ratio"] /= adjacent_total_pairs
+ _adjacent["content_words"]["std"] = np.std(
+ np.array(content_word_overlap_adjacent)
+ )
+ for cat in _other_adjacent:
+ _other_adjacent[cat]["avg_ratio"] /= adjacent_total_pairs
+ for cat in [k for k in _adjacent if k != "content_words"]:
+ _adjacent[cat] /= adjacent_total_pairs
+ _adjacent["other"] = _other_adjacent
+
+ if max_sentences_reached:
+ return {"adjacent": _adjacent, "global": None}
+
+ # finalize global metrics
+ if global_total_pairs > 0:
+ _global["content_words"]["ratio"] /= global_total_pairs
+ _global["content_words"]["std"] = np.std(
+ np.array(content_word_overlap_global)
+ )
+ for cat in _other_global:
+ _other_global[cat]["avg_ratio"] /= global_total_pairs
+ for cat in [k for k in _global if k != "content_words"]:
+ _global[cat] /= global_total_pairs
+ _global["other"] = _other_global
+
+ return {"adjacent": _adjacent, "global": _global}
+