explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-26 18:06:29 +03:00
Code Issues Packages Projects Releases Wiki Activity

* Prevent supersenses from being assigned to CONJ, DET, NUM and PRON words.

Browse Source
This commit is contained in:
Matthew Honnibal 2015-07-05 14:20:07 +02:00
parent 9534d336ed
commit 3eff39ff63
1 changed files with 26 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
spacy/sense_tagger.pyx
View File

@ -255,10 +255,10 @@ cdef class SenseTagger:
self.pos_senses[<int>parts_of_speech.ADJ] = all_senses
self.pos_senses[<int>parts_of_speech.ADV] = all_senses
self.pos_senses[<int>parts_of_speech.ADP] = all_senses
self.pos_senses[<int>parts_of_speech.CONJ] = all_senses
self.pos_senses[<int>parts_of_speech.DET] = all_senses
self.pos_senses[<int>parts_of_speech.NUM] = all_senses
self.pos_senses[<int>parts_of_speech.PRON] = all_senses
self.pos_senses[<int>parts_of_speech.CONJ] = 0
self.pos_senses[<int>parts_of_speech.DET] = 0
self.pos_senses[<int>parts_of_speech.NUM] = 0
self.pos_senses[<int>parts_of_speech.PRON] = 0
self.pos_senses[<int>parts_of_speech.PRT] = all_senses
self.pos_senses[<int>parts_of_speech.X] = all_senses
self.pos_senses[<int>parts_of_speech.PUNCT] = 0
@ -286,9 +286,11 @@ cdef class SenseTagger:
local_feats = self.extractor.get_feats(local_context, &n_feats)
features.extend(local_feats, n_feats)
scores = self.model.get_scores(features.c, features.length)
self.weight_scores_by_tagdict(<weight_t*><void*>scores, token, 0.95)
self.weight_scores_by_tagdict(<weight_t*><void*>scores, token, 0.9)
tokens.data[i].sense = self.best_in_set(scores, valid_senses)
features.clear()
else:
token.sense = NO_SENSE
def train(self, Tokens tokens):
cdef int i, j
@ -303,26 +305,32 @@ cdef class SenseTagger:
token = &tokens.data[i]
pos_senses = self.pos_senses[<int>token.pos]
lex_senses = token.lex.senses & pos_senses
if pos_senses >= 2 and lex_senses >= 2:
if lex_senses >= 2:
fill_context(context, token)
feats = self.extractor.get_feats(context, &n_feats)
scores = self.model.get_scores(feats, n_feats)
#self.weight_scores_by_tagdict(<weight_t*><void*>scores, token, 0.1)
guess = self.best_in_set(scores, pos_senses)
best = self.best_in_set(scores, lex_senses)
guess_counts = {}
gold_counts = {}
if guess != best:
cost += 1
for j in range(n_feats):
f_key = feats[j].key
f_i = feats[j].i
feat = (f_i, f_key)
gold_counts[feat] = gold_counts.get(feat, 0) + 1.0
guess_counts[feat] = guess_counts.get(feat, 0) - 1.0
self.model.update({guess: guess_counts, best: gold_counts})
update = self._make_update(feats, n_feats, guess, best)
self.model.update(update)
token.sense = best
cost += guess != best
else:
token.sense = 1
return cost
cdef dict _make_update(self, const Feature* feats, int n_feats, int guess, int best):
guess_counts = {}
gold_counts = {}
if guess != best:
for j in range(n_feats):
f_key = feats[j].key
f_i = feats[j].i
feat = (f_i, f_key)
gold_counts[feat] = gold_counts.get(feat, 0) + 1.0
guess_counts[feat] = guess_counts.get(feat, 0) - 1.0
return {guess: guess_counts, best: gold_counts}
cdef int best_in_set(self, const weight_t* scores, flags_t senses) except -1:
cdef weight_t max_ = 0
cdef int argmax = -1