Spacy matching priority - nlp

Question

I'm looking to create a physics pattern library with spacy:
I want to detect time and speed pattern. My aim is to stay flexible with those pattern.
time_pattern = [
[
{'LIKE_NUM': True, 'OP': '?'},
{'LOWER':{'IN': ['time', 's','h','min']}},
{'LOWER': {"IN": ['maximum','minimum','min','max']}, 'OP':'?'}
]
]
speed_pattern = [
[
{'LIKE_NUM': True, 'OP': '?'},
{'LOWER':{"IN": ['km', 'm']}},
{'IS_PUNCT': True},
{'LOWER' : {"IN": ['h','hour','s','min']}}
]
]
matcher=Matcher(nlp.vocab, validate =True)
matcher.add("SPEED", speed_pattern)
matcher.add("TIME", time_pattern)
doc=nlp("a certain time, more about 23 min, can't get above 25 km/h")
for id_match, start, end in matcher(doc):
match_label=nlp.vocab[id_match].text
print(match_label, '<--', doc[start:end])
So far my code returns this collection of matches:
TIME <-- time
TIME <-- 23 min
TIME <-- min
SPEED <-- 25 km/h
SPEED<-- km/h
TIME <-- h
I want the matcher to match only once, and to match "23 min" rather than "min". Also would like the matcher not to match an element already matched ( for exemple "h" should not be matched because it already matched in "km/h"

Answer 1

You can try add greedy="LONGEST" to matcher.add() to return only the longest (or FIRST) matches:
matcher.add("SPEED", speed_pattern, greedy="LONGEST")
matcher.add("TIME", time_pattern, greedy="LONGEST")
But note that this doesn't handle overlaps across different match IDs:
TIME <-- 23 min
TIME <-- time
TIME <-- h
SPEED <-- 25 km/h
If you want to filter all of the matches, you can use matcher(doc, as_spans=True) to get the matches directly as spans and then use spacy.util.filter_spans to filter the whole list of spans to a list of non-overlapping spans with the longest spans preferred: https://spacy.io/api/top-level#util.filter_spans
[time, 23 min, 25 km/h]

Answer 2

You can use as_spans=True option with spacy.matcher.Matcher (introduced in spaCy v3.0):
matches = matcher(doc, as_spans=True)
for span in spacy.util.filter_spans(matches):
print(span.label_, "->", span.text)
From the documentation:
Instead of tuples, return a list of Span objects of the matches, with the match_id assigned as the span label. Defaults to False.
See the Python demo:
import spacy
from spacy.tokens.doc import Doc
from spacy.matcher import Matcher
nlp = spacy.load('en_core_web_sm')
time_pattern = [
[
{'LIKE_NUM': True, 'OP': '?'},
{'LOWER':{'IN': ['time', 's','h','min']}},
{'LOWER': {"IN": ['maximum','minimum','min','max']}, 'OP':'?'}
]
]
speed_pattern = [
[
{'LIKE_NUM': True, 'OP': '?'},
{'LOWER':{"IN": ['km', 'm']}},
{'IS_PUNCT': True},
{'LOWER' : {"IN": ['h','hour','s','min']}}
]
]
matcher=Matcher(nlp.vocab, validate =True)
matcher.add("SPEED", speed_pattern)
matcher.add("TIME", time_pattern)
doc=nlp("a certain time, more about 23 min, can't get above 25 km/h")
matches = matcher(doc, as_spans=True)
for span in spacy.util.filter_spans(matches):
print(span.label_, "->", span.text)
Output:
TIME -> time
TIME -> 23 min
SPEED -> 25 km/h