def evaluate_instance(inst, classifier, tagger):
# Get the supervised POS tags...
"""
:param inst:
:type inst: RGIgt
:param classifier: MalletMaxent
:param tagger: StanfordPOSTagger
"""
sup_gloss_tier = pos_tag_tier(inst, GLOSS_WORD_ID) # We will incrementally build up the tag sequences...
sup_lang_tier = pos_tag_tier(inst, LANG_WORD_ID)
sup_tags = []
prj_tags = []
cls_tags = []
# If there are no supervised tags on the gloss line, but there are on the language line...
if sup_gloss_tier is None and sup_lang_tier is not None:
try:
add_gloss_lang_alignments(inst)
project_lang_to_gloss(inst)
sup_gloss_tier = pos_tag_tier(inst, GLOSS_WORD_ID)
except RGXigtException:
pass
if sup_gloss_tier:
# Do the classification
classify_gloss_pos(inst, classifier)
cls_tier = pos_tag_tier(inst, GLOSS_WORD_ID, tag_method=INTENT_POS_CLASS)
for sup_item in sup_gloss_tier:
word = xigt_find(inst, id=sup_item.alignment)
if not word:
continue
else:
word = word.value()
# prj_item = xigt_find(prj_tier, alignment=sup_item.alignment)
# if prj_item is None:
# prj_tag = 'UNK'
# else:
# prj_tag = prj_item.value()
cls_item = xigt_find(cls_tier, alignment=sup_item.alignment)
if cls_item is None:
cls_tag = 'UNK'
else:
cls_tag = cls_item.value()
sup_tags.append(POSToken(word, label=sup_item.value()))
# prj_tags.append(POSToken(word, label=prj_tag))
cls_tags.append(POSToken(word, label=cls_tag))
return sup_tags, cls_tags
def gather_gloss_pos_stats(inst, subword_dict, feat_list):
"""
Given an instance, look for the gloss pos tags, and save the statistics
about them, so that we can filter by the number of times each kind was
seen later.
:param inst: Instance to process.
:type inst: RGIgt
:param subword_dict: This dictionary will record the number of times each (word, TAG)
pair has been seen.
:type subword_dict: SubwordDict
:param gram_tag_dict: This dictionary will record the number of times individual grams are seen.
:type gram_tag_dict: TwoLevelCountDict
"""
# Grab the gloss POS tier...
gpos_tier = gloss_tag_tier(inst)
lpos_tier = lang_tag_tier(inst)
gw_tier = gloss(inst)
if CLASS_FEATS_ALN in feat_list:
heur_align_inst(inst)
get_trans_glosses_alignment(inst, aln_method=INTENT_ALN_HEUR)
# If there are POS tags on the language line but not the gloss line...
if gpos_tier is None and lpos_tier is not None:
add_gloss_lang_alignments(inst)
project_lang_to_gloss(inst)
gpos_tier = gloss_tag_tier(inst)
# If this tier exists, then let's process it.
if gpos_tier is not None:
# Iterate over each gloss POS tag...
for i, gw in enumerate(gw_tier):
tag = xigt_find(inst, alignment=gw.id)
if tag is None:
continue
prev_word = gw_tier[i-1].value().lower() if i > 0 else None
next_word = gw_tier[i+1].value().lower() if i < len(gw_tier)-1 else None
if CLASS_FEATS_ALN in feat_list:
subword_dict.add_word_tag(gw.value().lower(), tag.value(), prev_word, next_word)
