# cython: profile=True # cython: embedsignature=True '''Tokenize English text, using a scheme that differs from the Penn Treebank 3 scheme in several important respects: * Whitespace is added as tokens, except for single spaces. e.g., >>> [w.string for w in EN.tokenize(u'\\nHello \\tThere')] [u'\\n', u'Hello', u' ', u'\\t', u'There'] * Contractions are normalized, e.g. >>> [w.string for w in EN.tokenize(u"isn't ain't won't he's")] [u'is', u'not', u'are', u'not', u'will', u'not', u'he', u"__s"] * Hyphenated words are split, with the hyphen preserved, e.g.: >>> [w.string for w in EN.tokenize(u'New York-based')] [u'New', u'York', u'-', u'based'] Other improvements: * Email addresses, URLs, European-formatted dates and other numeric entities not found in the PTB are tokenized correctly * Heuristic handling of word-final periods (PTB expects sentence boundary detection as a pre-process before tokenization.) Take care to ensure your training and run-time data is tokenized according to the same scheme. Tokenization problems are a major cause of poor performance for NLP tools. If you're using a pre-trained model, the :py:mod:`spacy.ptb3` module provides a fully Penn Treebank 3-compliant tokenizer. ''' from __future__ import unicode_literals from murmurhash.mrmr cimport hash64 cimport lang from .typedefs cimport hash_t, id_t, flags_t import orth from .morphology cimport NO_TAG, ADJ, ADV, ADP, CONJ, DET, NOUN, NUM, PRON, PRT, VERB from .morphology cimport X, PUNCT, EOL from .tokens cimport Morphology DEF USE_POS_CACHE = True POS_TAGS = { 'NULL': (NO_TAG, {}), 'EOL': (EOL, {}), 'CC': (CONJ, {}), 'CD': (NUM, {}), 'DT': (DET, {}), 'EX': (DET, {}), 'FW': (X, {}), 'IN': (ADP, {}), 'JJ': (ADJ, {}), 'JJR': (ADJ, {'misc': COMPARATIVE}), 'JJS': (ADJ, {'misc': SUPERLATIVE}), 'LS': (X, {}), 'MD': (VERB, {'tenspect': MODAL}), 'NN': (NOUN, {}), 'NNS': (NOUN, {'number': PLURAL}), 'NNP': (NOUN, {'misc': NAME}), 'NNPS': (NOUN, {'misc': NAME, 'number': PLURAL}), 'PDT': (DET, {}), 'POS': (PRT, {'case': GENITIVE}), 'PRP': (NOUN, {}), 'PRP$': (NOUN, {'case': GENITIVE}), 'RB': (ADV, {}), 'RBR': (ADV, {'misc': COMPARATIVE}), 'RBS': (ADV, {'misc': SUPERLATIVE}), 'RP': (PRT, {}), 'SYM': (X, {}), 'TO': (PRT, {}), 'UH': (X, {}), 'VB': (VERB, {}), 'VBD': (VERB, {'tenspect': PAST}), 'VBG': (VERB, {'tenspect': ING}), 'VBN': (VERB, {'tenspect': PASSIVE}), 'VBP': (VERB, {'tenspect': PRESENT}), 'VBZ': (VERB, {'tenspect': PRESENT, 'person': THIRD}), 'WDT': (DET, {'misc': RELATIVE}), 'WP': (PRON, {'misc': RELATIVE}), 'WP$': (PRON, {'misc': RELATIVE, 'case': GENITIVE}), 'WRB': (ADV, {'misc': RELATIVE}), '!': (PUNCT, {}), '#': (PUNCT, {}), '$': (PUNCT, {}), "''": (PUNCT, {}), "(": (PUNCT, {}), ")": (PUNCT, {}), "-LRB-": (PUNCT, {}), "-RRB-": (PUNCT, {}), ".": (PUNCT, {}), ",": (PUNCT, {}), "``": (PUNCT, {}), ":": (PUNCT, {}), "?": (PUNCT, {}), } POS_TEMPLATES = ( (W_sic,), (P1_lemma, P1_pos), (P2_lemma, P2_pos), (N1_sic,), (N2_sic,), (W_suffix,), (W_prefix,), (P1_pos,), (P2_pos,), (P1_pos, P2_pos), (P1_pos, W_sic), (P1_suffix,), (N1_suffix,), (W_shape,), (W_cluster,), (N1_cluster,), (N2_cluster,), (P1_cluster,), (P2_cluster,), (W_pos_type,), (N1_pos_type,), (N1_pos_type,), (P1_pos, W_pos_type, N1_pos_type), ) cdef class English(Language): """English tokenizer, tightly coupled to lexicon. Attributes: name (unicode): The two letter code used by Wikipedia for the language. lexicon (Lexicon): The lexicon. Exposes the lookup method. """ def get_props(self, unicode string): return {'flags': self.set_flags(string), 'dense': orth.word_shape(string)} def set_flags(self, unicode string): cdef flags_t flags = 0 flags |= orth.is_alpha(string) << IS_ALPHA flags |= orth.is_ascii(string) << IS_ASCII flags |= orth.is_digit(string) << IS_DIGIT flags |= orth.is_lower(string) << IS_LOWER flags |= orth.is_punct(string) << IS_PUNCT flags |= orth.is_space(string) << IS_SPACE flags |= orth.is_title(string) << IS_TITLE flags |= orth.is_upper(string) << IS_UPPER flags |= orth.like_url(string) << LIKE_URL flags |= orth.like_number(string) << LIKE_NUMBER return flags def set_pos(self, Tokens tokens): cdef int i cdef atom_t[N_CONTEXT_FIELDS] context cdef TokenC* t = tokens.data cdef id_t[2] bigram cdef hash_t cache_key cdef void* cached = NULL assert self.morphologizer is not None cdef dict tagdict = self.pos_tagger.tagdict for i in range(tokens.length): fill_pos_context(context, i, t) t[i].pos = self.pos_tagger.predict(context) self.morphologizer.set_morph(i, t) def train_pos(self, Tokens tokens, golds): cdef int i cdef atom_t[N_CONTEXT_FIELDS] context c = 0 cdef TokenC* t = tokens.data for i in range(tokens.length): fill_pos_context(context, i, t) t[i].pos = self.pos_tagger.predict(context, [golds[i]]) self.morphologizer.set_morph(i, t) c += t[i].pos == golds[i] return c cdef int is_base_np_end(self, const TokenC* token) except -1: pass cdef int is_outside_base_np(self, const TokenC* token) except -1: pass cdef int fill_pos_context(atom_t* context, const int i, const TokenC* tokens) except -1: _fill_from_token(&context[P2_sic], &tokens[i-2]) _fill_from_token(&context[P1_sic], &tokens[i-1]) _fill_from_token(&context[W_sic], &tokens[i]) _fill_from_token(&context[N1_sic], &tokens[i+1]) _fill_from_token(&context[N2_sic], &tokens[i+2]) cdef inline void _fill_from_token(atom_t* context, const TokenC* t) nogil: context[0] = t.lex.sic context[1] = t.lex.cluster context[2] = t.lex.shape context[3] = t.lex.prefix context[4] = t.lex.suffix context[5] = t.pos context[6] = t.lemma context[7] = t.lex.pos_type EN = English('en')