我有一个文本文件,并且实现了Polyglot NER以从该文本文件中提取实体。然后,我必须对每个句子进行分段,并匹配每个句子上提取的实体。匹配时,它应该给我输出。
from polyglot.text import Text
file = open('input_raw.txt', 'r')
input_file = file.read()
file = Text(input_file, hint_language_code='fa')
def return_match(entities_list, sentence): ## Check if Chunks
for term in entities_list: ## are in any of the entities
## Check each list in each Chunk object
## and see if there's any matches.
for entity in sentence.entities:
if entity == term:
return entity
return None
def return_list_of_entities(file):
list_entity = []
for sentence in file.sentences:
for entity in sentence.entities:
list_entity.append(entity)
return list_entity
list_entity = return_list_of_entities(file)
#sentence_number = 4 # Which sentence to check
for sentence in range(len(file.sentences)):
sentencess = file.sentences[sentence]
match = return_match(list_entity, sentencess)
if match is not None:
print("Entity Term " + str(match) +
" is in the sentence. '" + str(sentencess)+ "'")
else:
print("Sentence '" + str(sentencess) +
"' doesn't contain any of the terms" + str(list_entity))
input_file:
Bill Gates is the founder of Microsoft.
Trump is the president of the USA.
Bill Gates was a student in Harvard.
当我们实现NER时,实体看起来像:
list_etity:
Bill Gates, Microsoft, Trump, USA, Bill Gate, Harvard
当我们将实体与第一句话匹配时,它给出:
当前输出:
(Bill Gates, Bill Gates, Microsoft)
预期输出:
(Bill Gates, Microsoft) # this is from the first sentence and should contine
(Trump, USA)
(Bill Gates, Harvard)
答案 0 :(得分:0)
//sampler for the texture
val sampler = Texture.Sampler.builder()
.setWrapMode(Texture.Sampler.WrapMode.REPEAT)
.build()
Texture.builder()
.setSampler(sampler)
.setSource(this, R.drawable.your_drawable_texture)
.build()
.thenCompose { texture ->
MaterialFactory.makeOpaqueWithTexture(this, texture)
}
.thenAccept { material ->
ShapeFactory.makeCube(vector, vector, material)
}