# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """UniMorph: Universal Morphologies.""" import datasets _CITATION = """\ @article{sylak2016composition, title={The composition and use of the universal morphological feature schema (unimorph schema)}, author={Sylak-Glassman, John}, journal={Johns Hopkins University}, year={2016} } """ _DESCRIPTION = """\ The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema. The specification of the schema is described in Sylak-Glassman (2016). 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"https://raw.githubusercontent.com/unimorph/vep/7f9b0934b8831793929e219baa7d6f0cbc06e32f/vep-central-western-veps.txt", "vep|new_written": "https://raw.githubusercontent.com/unimorph/vep/7f9b0934b8831793929e219baa7d6f0cbc06e32f/vep-new-written-veps.txt", "vep|northern": "https://raw.githubusercontent.com/unimorph/vep/7f9b0934b8831793929e219baa7d6f0cbc06e32f/vep-northern-veps.txt", "vep|southern": "https://raw.githubusercontent.com/unimorph/vep/7f9b0934b8831793929e219baa7d6f0cbc06e32f/vep-southern-veps.txt", } _LANGUAGES = sorted(set([ln.split("|")[0] for ln in _URLs])) # make multiple splits for the languages with several dialects _SPLITS = {} for ln in _URLs: if "|" in ln: ln_conf, ln_split = ln.split("|") _SPLITS[ln_conf] = _SPLITS.get(ln_conf, []) + [ln_split] _CATEGORIES = { "Aktionsart": ["STAT", "DYN", "TEL", "ATEL", "PCT", "DUR", "ACH", "ACCMP", "SEMEL", "ACTY"], "Animacy": ["ANIM", "INAN", "HUM", "NHUM"], "Argument_Marking": [ "ARGNO1S", "ARGNO2S", "ARGNO3S", "ARGNO1P", "ARGNO2P", "ARGNO3P", "ARGAC1S", "ARGAC2S", "ARGAC3S", "ARGAC1P", "ARGAC2P", "ARGAC3P", "ARGAB1S", "ARGAB2S", "ARGAB3S", "ARGAB1P", "ARGAB2P", "ARGAB3P", "ARGER1S", "ARGER2S", "ARGER3S", "ARGER1P", "ARGER2P", "ARGER3P", "ARGDA1S", "ARGDA2S", "ARGDA3S", "ARGDA1P", "ARGDA2P", "ARGDA3P", "ARGBE1S", "ARGBE2S", "ARGBE3S", "ARGBE1P", "ARGBE2P", "ARGBE3P", ], "Aspect": ["IPFV", "PFV", "PRF", "PROG", "PROSP", "ITER", "HAB"], "Case": [ "NOM", "ACC", "ERG", "ABS", "NOMS", "DAT", "BEN", "PRP", "GEN", "REL", "PRT", "INS", "COM", "VOC", "COMPV", "EQTV", "PRIV", "PROPR", "AVR", "FRML", "TRANS", "BYWAY", "INTER", "AT", "POST", "IN", "CIRC", "ANTE", "APUD", "ON", "ONHR", "ONVR", "SUB", "REM", "PROXM", "ESS", "ALL", "ABL", "APPRX", "TERM", ], "Comparison": ["CMPR", "SPRL", "AB", "RL", "EQT"], "Definiteness": ["DEF", "INDF", "SPEC", "NSPEC"], "Deixis": ["PROX", "MED", "REMT", "REF1", "REF2", "NOREF", "PHOR", "VIS", "NVIS", "ABV", "EVEN", "BEL"], "Evidentiality": ["FH", "DRCT", "SEN", "VISU", "NVSEN", "AUD", "NFH", "QUOT", "RPRT", "HRSY", "INFER", "ASSUM"], "Finiteness": ["FIN", "NFIN"], "Gender": [ "MASC", "FEM", "NEUT", "NAKH1", "NAKH2", "NAKH3", "NAKH4", "NAKH5", "NAKH6", "NAKH7", "NAKH8", "BANTU1", "BANTU2", "BANTU3", "BANTU4", "BANTU5", "BANTU6", "BANTU7", "BANTU8", "BANTU9", "BANTU10", "BANTU11", "BANTU12", "BANTU13", "BANTU14", "BANTU15", "BANTU16", "BANTU17", "BANTU18", "BANTU19", "BANTU20", "BANTU21", "BANTU22", "BANTU23", ], "Information_Structure": ["TOP", "FOC"], "Interrogativity": ["DECL", "INT"], "Language_Specific": [ "LGSPEC1", "LGSPEC2", "LGSPEC3", "LGSPEC4", "LGSPEC5", "LGSPEC6", "LGSPEC7", "LGSPEC8", "LGSPEC9", "LGSPEC10", ], "Mood": [ "IND", "SBJV", "REAL", "IRR", "AUPRP", "AUNPRP", "IMP", "COND", "PURP", "INTEN", "POT", "LKLY", "ADM", "OBLIG", "DEB", "PERM", "DED", "SIM", "OPT", ], "Number": ["SG", "PL", "GRPL", "DU", "TRI", "PAUC", "GRPAUC", "INVN"], "Part_Of_Speech": [ "N", "PROPN", "ADJ", "PRO", "CLF", "ART", "DET", "V", "ADV", "AUX", "V.PTCP", "V.MSDR", "V.CVB", "ADP", "COMP", "CONJ", "NUM", "PART", "INTJ", ], "Person": ["0", "1", "2", "3", "4", "INCL", "EXCL", "PRX", "OBV"], "Polarity": ["POS", "NEG"], "Politeness": [ "INFM", "FORM", "ELEV", "HUMB", "POL", "AVOID", "LOW", "HIGH", "STELEV", "STSUPR", "LIT", "FOREG", "COL", ], "Possession": [ "ALN", "NALN", "PSS1S", "PSS2S", "PSS2SF", "PSS2SM", "PSS2SINFM", "PSS2SFORM", "PSS3S", "PSS3SF", "PSS3SM", "PSS1D", "PSS1DI", "PSS1DE", "PSS2D", "PSS2DM", "PSS2DF", "PSS3D", "PSS3DF", "PSS3DM", "PSS1P", "PSS1PI", "PSS1PE", "PSS2P", "PSS2PF", "PSS2PM", "PSS3PF", "PSS3PM", ], "Switch_Reference": ["SS", "SSADV", "DS", "DSADV", "OR", "SIMMA", "SEQMA", "LOG"], "Tense": ["PRS", "PST", "FUT", "IMMED", "HOD", "1DAY", "RCT", "RMT"], "Valency": ["IMPRS", "INTR", "TR", "DITR", "REFL", "RECP", "CAUS", "APPL"], "Voice": ["ACT", "MID", "PASS", "ANTIP", "DIR", "INV", "AGFOC", "PFOC", "LFOC", "BFOC", "ACFOC", "IFOC", "CFOC"], } _TAG_TO_CAT = dict([(tag, cat) for cat, tags in _CATEGORIES.items() for tag in tags]) class UniversalMorphologies(datasets.GeneratorBasedBuilder): """UniMorph: Universal Morphologies.""" BUILDER_CONFIGS = [ datasets.BuilderConfig( name=f"{language_iso}", version=datasets.Version("1.0.0"), description=f"Universal Morphologies for {language_iso}", ) for language_iso in _LANGUAGES ] DEFAULT_CONFIG_NAME = "ady" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): features = datasets.Features( { "lemma": datasets.Value("string"), "forms": datasets.Sequence( dict( [("word", datasets.Value("string"))] + [ (cat, datasets.Sequence(datasets.ClassLabel(names=tasks))) for cat, tasks in _CATEGORIES.items() ] + [("Other", datasets.Sequence(datasets.Value("string")))] # for misspecified tags ) ), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, # Here we define them above because they are different between the two configurations supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" config_urls = dict([(ln, f_url) for ln, f_url in _URLs.items() if ln.split("|")[0] == self.config.name]) data_dir = dl_manager.download(config_urls) if self.config.name in _SPLITS: return [ datasets.SplitGenerator( name=spl, gen_kwargs={ "filepath": data_dir[f"{self.config.name}|{spl}"], }, ) for spl in _SPLITS[self.config.name] ] else: return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir[self.config.name], }, ) ] def _generate_examples(self, filepath): all_forms = {} # we need to do a full path first to gather all forms of a lemma with open(filepath, encoding="utf-8") as f: forms = [] for row in f: if row.strip() == "" or row.strip().startswith("#"): continue lemma, word, tags = row.strip().split("\t") all_forms[lemma] = all_forms.get(lemma, []) tag_list = tags.replace("NDEF", "INDF").split(";") form = dict([("word", word), ("Other", [])] + [(cat, []) for cat, tasks in _CATEGORIES.items()]) for tag_pre in tag_list: tag = tag_pre.split("+") if tag[0] in _TAG_TO_CAT: form[_TAG_TO_CAT[tag[0]]] = tag else: form["Other"] += tag all_forms[lemma] += [form] for id_, (lemma, forms) in enumerate(all_forms.items()): res = {"lemma": lemma, "forms": {}} for k in ["word", "Other"] + list(_CATEGORIES.keys()): res["forms"][k] = [form[k] for form in forms] yield id_, res