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685c2c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | from lingua.concept.linguagraph.standard import LinguaGraphNodePoses
from lingua.structure.gpgraph import GPGraph, GPGAuxNode, GPGPhraseNode, GPGNode
priority_map = {
"AttributePredicator": 80,
"AppositionalPredicator": 80,
"FactualPredicator": 80,
"LogicalPredicator": 80,
"ReferentialPredicator": 80,
"ConjunctionalFunctor": 80,
"ExpressionFunctor": 40,
"GeneralFunctor": 40,
"ListFunctor": 40,
"ModificationalFunctor": 80,
"DeterminerConstant": 80,
"InterjectionConstant": 80 ,
"ModificationalConstant": 80,
"NominalConstant": 80,
"OtherConstant": 80,
"PunctuationalConstant": 80,
"SymbolConstant": 80
}
def min_node_distance(n1: GPGNode, n2: GPGNode):
if isinstance(n1, GPGAuxNode) or isinstance(n2, GPGAuxNode):
return 10000
assert isinstance(n1, GPGPhraseNode) and isinstance(n2, GPGPhraseNode)
import numpy as np
n1_words = np.array(list(n1.words(with_aux=False)))
n2_words = np.array(list(n2.words(with_aux=False)))
from scipy.spatial.distance import cdist
return np.min(cdist(n1_words[..., np.newaxis], n2_words[..., np.newaxis])) # word index difference between n1 and n2
def get_modification_priority(graph: GPGraph, node, modification_phrases):
base_priority = 20
node_words = list(node.words(with_aux=False))
min_node_pos = min(node_words)
phrase_scores = []
for phrase in modification_phrases:
min_modification_pos = 10000
min_dist = 10000
for c in phrase:
min_dist = min(min_dist, min_node_distance(node, c))
if isinstance(c, GPGPhraseNode):
c_words = list(c.words(with_aux=False))
min_modification_pos = min(min_modification_pos, min(c_words))
# 如果修饰词在中心词之前，给予奖励；否则不奖励
positional_bonus = 100 if min_modification_pos < min_node_pos else 0
# 如果距离超过1，每超过1个 token 扣除一定分数
distance_penalty_factor = 5
distance_penalty = (min_dist - 1) * distance_penalty_factor if min_dist > 1 else 0
effective_score = base_priority + positional_bonus - distance_penalty
phrase_scores.append(effective_score)
return phrase_scores
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