how to write spacy matcher of POS regex

Question

Spacy has two features I'd like to combine - part-of-speech (POS) and rule-based matching.

How can I combine them in a neat way?

For example - let's say input is a single sentence and I'd like to verify it meets some POS ordering condition - for example the verb is after the noun (something like noun**verb regex). result should be true or false. Is that doable? or the matcher is specific like in the example

Rule-based matching can have POS rules?

If not - here is my current plan - gather everything in one string and apply regex

    import spacy
nlp = spacy.load('en')
#doc = nlp(u'is there any way you can do it')
text=u'what are the main issues'
doc = nlp(text)

concatPos = ''
print(text)
for word in doc:
    print(word.text, word.lemma, word.lemma_, word.tag, word.tag_, word.pos, word.pos_)
    concatPos += word.text +"_" + word.tag_ + "_" + word.pos_ + "-"
print('-----------')
print(concatPos)
print('-----------')

# output of string- what_WP_NOUN-are_VBP_VERB-the_DT_DET-main_JJ_ADJ-issues_NNS_NOUN-

Answer 1

Sure, simply use the POS attribute.

import spacy
nlp = spacy.load('en')
from spacy.matcher import Matcher
from spacy.attrs import POS
matcher = Matcher(nlp.vocab)
matcher.add_pattern("Adjective and noun", [{POS: 'ADJ'}, {POS: 'NOUN'}])

doc = nlp(u'what are the main issues')
matches = matcher(doc)

Answer 2

Eyal Shulman's answer was helpful, but it makes you hard code a pattern matcher, not exactly use a regular expression.

I wanted to use regular expressions, so I made my own solution:

    pattern = r'(<VERB>)*(<ADV>)*(<PART>)*(<VERB>)+(<PART>)*' 
    ## create a string with the pos of the sentence
    posString = ""
    for w in doc[start:end].sent:
        posString += "<" + w.pos_ + ">"

    lstVerb = []
    for m in re.compile(pattern).finditer(posString):
        ## each m is a verb phrase match
        ## count the "<" in m to find how many tokens we want
        numTokensInGroup = m.group().count('<')

        ## then find the number of tokens that came before that group.
        numTokensBeforeGroup = posString[:m.start()].count('<') 

        verbPhrase = sentence[numTokensBeforeGroup:numTokensBeforeGroup+numTokensInGroup]
        ## starting at character offset m.start()
        lstVerb.append(verbPhrase)