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* Add document features to sense_tagger.

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This commit is contained in:
Matthew Honnibal 2015-07-05 21:05:38 +02:00
parent 8f0fe1a4ea
commit cb628ba352
1 changed files with 69 additions and 28 deletions
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93
spacy/sense_tagger.pyx
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@ -57,6 +57,9 @@ cdef enum:
N2c6
N2c4
N3W
P3W
P1s
P2s
@ -131,6 +134,9 @@ unigrams = (
(P1s, P2s,),
(P1s, N0p),
(P1s, P2s, N0c),
(N3W,),
(P3W,),
)
@ -142,6 +148,9 @@ bigrams = (
(P1c6, N0p),
(N0p, N1p,),
(P2W, P1W),
(P1W, N1W),
(N1W, N2W),
)
@ -157,6 +166,7 @@ trigrams = (
(N0p, N1p, N2p),
(N0p, N1p,),
(N0c4, N1c4, N2c4),
(P1W, N0p, N0W),
)
@ -181,6 +191,8 @@ cdef int fill_context(atom_t* ctxt, const TokenC* token) except -1:
fill_token(&ctxt[N2W], token + 2)
ctxt[P1s] = (token - 1).sense
ctxt[P2s] = (token - 2).sense
ctxt[N3W] = (token + 3).lemma
ctxt[P3W] = (token - 3).lemma
cdef class FeatureVector:
@ -228,22 +240,19 @@ cdef class SenseTagger:
cdef dict tagdict
def __init__(self, StringStore strings, model_dir):
if model_dir is not None and path.isdir(model_dir):
model_dir = path.join(model_dir, 'wsd')
self.model_dir = model_dir
if path.exists(path.join(model_dir, 'supersenses.json')):
self.tagdict = json.load(open(path.join(model_dir, 'supersenses.json')))
if path.exists(path.join(model_dir, 'wordnet', 'supersenses.json')):
self.tagdict = json.load(open(path.join(model_dir, 'wordnet', 'supersenses.json')))
else:
self.tagdict = {}
if model_dir is not None and path.isdir(model_dir):
model_dir = path.join(model_dir, 'wsd')
templates = unigrams + bigrams + trigrams
self.extractor = Extractor(templates)
self.model = LinearModel(N_SENSES, self.extractor.n_templ)
model_loc = path.join(self.model_dir, 'model')
if model_loc and path.exists(model_loc):
self.model.load(model_loc, freq_thresh=0)
self.strings = strings
cdef flags_t all_senses = 0
cdef flags_t sense = 0
@ -277,18 +286,19 @@ cdef class SenseTagger:
cdef flags_t valid_senses = 0
cdef TokenC* token
cdef flags_t one = 1
cdef FeatureVector features = FeatureVector(100)
cdef int n_doc_feats
cdef Pool mem = Pool()
feats = self.get_doc_feats(mem, tokens, &n_doc_feats)
for i in range(tokens.length):
token = &tokens.data[i]
valid_senses = token.lex.senses & self.pos_senses[<int>token.pos]
if valid_senses >= 2:
fill_context(local_context, token)
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, 0.9)
n_local_feats = self.extractor.set_feats(&feats[n_doc_feats],
local_context)
scores = self.model.get_scores(feats, n_local_feats)
self.weight_scores_by_tagdict(<weight_t*><void*>scores, token, 0.0)
tokens.data[i].sense = self.best_in_set(scores, valid_senses)
features.clear()
else:
token.sense = NO_SENSE
@ -296,22 +306,27 @@ cdef class SenseTagger:
cdef int i, j
cdef TokenC* token
cdef atom_t[CONTEXT_SIZE] context
cdef int n_feats
cdef int n_doc_feats, n_local_feats
cdef feat_t f_key
cdef flags_t best_senses = 0
cdef int f_i
cdef int cost = 0
cdef Pool mem = Pool()
feats = self.get_doc_feats(mem, tokens, &n_doc_feats)
for i in range(tokens.length):
token = &tokens.data[i]
pos_senses = self.pos_senses[<int>token.pos]
lex_senses = token.lex.senses & pos_senses
if lex_senses >= 2:
fill_context(context, token)
feats = self.extractor.get_feats(context, &n_feats)
scores = self.model.get_scores(feats, n_feats)
n_local_feats = self.extractor.set_feats(&feats[n_doc_feats], context)
scores = self.model.get_scores(feats, n_doc_feats + n_local_feats)
guess = self.best_in_set(scores, pos_senses)
best = self.best_in_set(scores, lex_senses)
update = self._make_update(feats, n_feats, guess, best)
update = self._make_update(feats, n_doc_feats + n_local_feats,
guess, best)
self.model.update(update)
token.sense = best
cost += guess != best
@ -319,7 +334,7 @@ cdef class SenseTagger:
token.sense = 1
return cost
cdef dict _make_update(self, const Feature* feats, int n_feats, int guess, int best):
cdef dict _perceptron_update(self, const Feature* feats, int n_feats, int guess, int best):
guess_counts = {}
gold_counts = {}
if guess != best:
@ -331,6 +346,26 @@ cdef class SenseTagger:
guess_counts[feat] = guess_counts.get(feat, 0) - 1.0
return {guess: guess_counts, best: gold_counts}
cdef Feature* get_doc_feats(self, Pool mem, Tokens tokens, int* n_feats) except NULL:
# Get features for the document
# Start with activation strengths for each supersense
n_feats[0] = N_SENSES
feats = <Feature*>mem.alloc(n_feats[0] + self.extractor.n_templ + 1,
sizeof(Feature))
cdef int i, ssense
for ssense in range(N_SENSES):
feats[ssense] = Feature(i=0, key=ssense, value=0)
cdef flags_t pos_senses
cdef flags_t one = 1
for i in range(tokens.length):
sense_probs = self.tagdict.get(tokens.data[i].lemma, {})
pos_senses = self.pos_senses[<int>tokens.data[i].pos]
for ssense_str, prob in sense_probs.items():
ssense = int(ssense_str + 1)
if pos_senses & (one << <flags_t>ssense):
feats[ssense].value += prob
return feats
cdef int best_in_set(self, const weight_t* scores, flags_t senses) except -1:
cdef weight_t max_ = 0
cdef int argmax = -1
@ -343,18 +378,12 @@ cdef class SenseTagger:
assert argmax >= 0
return argmax
@cython.cdivision(True)
cdef int weight_scores_by_tagdict(self, weight_t* scores, const TokenC* token,
weight_t a) except -1:
lemma = self.strings[token.lemma]
# First softmax the scores
cdef int i
cdef double total = 0
for i in range(N_SENSES):
total += exp(scores[i])
for i in range(N_SENSES):
scores[i] = <weight_t>(exp(scores[i]) / total)
softmax(scores, N_SENSES)
probs = self.tagdict.get(lemma, {})
for i in range(1, N_SENSES):
@ -365,6 +394,18 @@ cdef class SenseTagger:
self.model.end_training()
self.model.dump(path.join(self.model_dir, 'model'), freq_thresh=0)
@cython.cdivision(True)
cdef void softmax(weight_t* scores, int n_classes) nogil:
cdef int i
cdef double total = 0
for i in range(N_SENSES):
total += exp(scores[i])
for i in range(N_SENSES):
scores[i] = <weight_t>(exp(scores[i]) / total)
cdef list _set_bits(flags_t flags):
bits = []
cdef flags_t bit