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87 lines
2.2 KiB
87 lines
2.2 KiB
from lxml import etree
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import re
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W_TAGS = ['w']
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C_TAGS = ['c']
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S_TAGS = ['S', 'pc']
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# reads a TEI xml file and returns a dictionary:
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# { <sentence_id>: {
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# sid: <sentence_id>, # serves as index in MongoDB
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# text: ,
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# tokens: ,
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# }}
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def parse_tei(filepath):
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guess_corpus = None # SSJ | KRES
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res_dict = {}
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with open(filepath, "r") as fp:
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# remove namespaces
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xmlstr = fp.read()
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xmlstr = re.sub('\\sxmlns="[^"]+"', '', xmlstr, count=1)
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xmlstr = re.sub(' xml:', ' ', xmlstr)
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root = etree.XML(xmlstr.encode("utf-8"))
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divs = [] # in ssj, there are divs, in Kres, there are separate files
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if "id" in root.keys():
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# Kres files start with <TEI id=...>
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guess_corpus = "KRES"
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divs = [root]
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else:
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guess_corpus = "SSJ"
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divs = root.findall(".//div")
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# parse divs
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for div in divs:
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f_id = div.get("id")
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# parse paragraphs
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for p in div.findall(".//p"):
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p_id = p.get("id").split(".")[-1]
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# parse sentences
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for s in p.findall(".//s"):
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s_id = s.get("id").split(".")[-1]
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sentence_text = ""
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sentence_tokens = []
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# parse tokens
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for el in s.iter():
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if el.tag in W_TAGS:
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el_id = el.get("id").split(".")[-1]
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if el_id[0] == 't':
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el_id = el_id[1:] # ssj W_TAG ids start with t
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sentence_text += el.text
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sentence_tokens += [(
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"w",
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el_id,
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el.text,
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el.get("lemma"),
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(el.get("msd") if guess_corpus == "KRES" else el.get("ana").split(":")[-1]),
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)]
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elif el.tag in C_TAGS:
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el_id = el.get("id") or "none" # only Kres' C_TAGS have ids
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el_id = el_id.split(".")[-1]
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sentence_text += el.text
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sentence_tokens += [("c", el_id, el.text,)]
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elif el.tag in S_TAGS:
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sentence_text += " " # Kres' <S /> doesn't contain .text
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else:
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# pass links and linkGroups
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# print(el.tag)
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pass
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sentence_id = "{}.{}.{}".format(f_id, p_id, s_id)
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"""
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print(sentence_id)
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print(sentence_text)
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print(sentence_tokens)
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"""
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if sentence_id in res_dict:
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raise KeyError("duplicated id: {}".format(sentence_id))
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res_dict[sentence_id] = {
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"sid": sentence_id,
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"text": sentence_text,
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"tokens": sentence_tokens
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}
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return res_dict
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