forked from kristjan/cjvt-srl-tagging
152 lines
4.9 KiB
Python
152 lines
4.9 KiB
Python
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from lxml import etree
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import re
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from parser.msdmap import Msdmap
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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, "rb") as fp:
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# remove namespaces
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bstr = fp.read()
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utf8str = bstr.decode("utf-8")
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utf8str = re.sub('\\sxmlns="[^"]+"', '', utf8str, count=1)
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utf8str = re.sub(' xml:', ' ', utf8str)
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root = etree.XML(utf8str.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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int(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"
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else el.get("ana").split(":")[-1]),
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)]
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elif el.tag in C_TAGS:
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# only Kres' C_TAGS have ids
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el_id = el.get("id") or "none"
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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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# Kres' <S /> doesn't contain .text
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sentence_text += " "
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else:
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# pass links and linkGroups
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pass
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sentence_id = "{}.{}.{}".format(f_id, p_id, s_id)
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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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"links": (
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parse_links(s) if guess_corpus == "KRES" else None
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)
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}
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return res_dict
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def parse_links(s_el):
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lgrps = s_el.findall(".//links")
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if len(lgrps) < 1:
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raise IOError("Can't find links.")
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res_links = {}
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for link in lgrps[0]:
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dep = int(link.get("dep").split(".")[-1])
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res_links[dep] = (
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link.get("afun"),
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dep,
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int(link.get("from").split(".")[-1]),
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)
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return res_links
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def to_conll09(sentence_entry):
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def fillpred(pos, feat):
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if False:
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# todo
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return "Y"
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return "_"
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msdm = Msdmap()
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# works with kres, with parsed links
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out_str = ""
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for token in sentence_entry["tokens"]:
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if token[0] != "w":
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continue
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msd = msdm.msd_from_slo(token[4])
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fprd = fillpred("todo", "todo")
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print(msd)
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print(token)
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print(sentence_entry["links"])
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t_id = token[1]
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print(t_id)
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# 1 3 4 5 6 7 8 9 10 11 12 13 14
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out_str += "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
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t_id, # id
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token[2], # form
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token[3], # lemma
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token[3], # plemma
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"todo", # pos (TODO)
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"todo", # ppos (TODO)
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"todo", # feat (TODO)
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"todo", # pfeat (TODO)
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sentence_entry["links"][t_id][2], # head
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sentence_entry["links"][t_id][2], # phead
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sentence_entry["links"][t_id][1], # deprel
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sentence_entry["links"][t_id][1], # pdeprel
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fprd, # fillpred
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(token[3] if fprd == "Y" else "_"), # pred
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"todo" # apredn...
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)
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out_str += "\n"
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return out_str
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