自然语言数据标注方法(脚本)
⾃然语⾔数据标注⽅法（脚本）
本数据主要⽤于评估⾃然语⾔单词和程序语⾔API之间的相关性。
每⼀个配对中包含⼀个单词和API，如果两者之间相关性判定为相关则标注为1，如果判定为不相关则标注为0。
判断标准： 主要根据单词的含义和API包含的功能进⾏判断，如果API包含的功能涉及单词的含义，则可认为单词与API相关。
例如，对于名词“bean”，如果API涉及对于bean的操作或者含有bean的属性等则认为⼆者相关；对于动词“exchange”，如果API的功能中包含对数据进⾏接收和发送的动作等，则认为⼆者相关。
标注数据⽰例
根据word的单词，到对应句⼦API中，是否有意思相近的词，如果有相近意思单词，rel输出1，否则输出0。
案例主要将current意思相近的词，标注1，否则标注0
"current"近义词：["current","present","existing","recent","up-to-date","contemporary","present-day","modern","in progress","up to date","dated"]
import pandas as pd
# 查看api列中是否有word列的近义词
# 安装pandas包将csv⽂件与test.py放在同⼀⽬录下执⾏
data_map = {
# 要标注的词 load, load的近义词 get load read import
# 改改改改成相关要修改的近义词,
"current":["current","present","existing","recent","up-to-date","contemporary","present-day","modern","in progress","up to date","dated"],    "agent":["agent","go-between","manager","negotiator","mediator","representative","proxy"],
"cache":["board","store","supply","accumulation","reserve","collection"],
"mode": ["mode", "pattern", "model"],
梦见别人请吃饭"message": ["message", "uri", "url", "trace", "print","get"]
}
# 改改改要标注的⽂件名
src_name = "18.csv"
尹正整容>依山尽# 标注完之后⽣成的⽂件名
你到底爱上谁target_name = "18answer.csv"
def find_rel(arr):
word = arr[0]
api = arr[1].upper()
rel = arr[2]
for word_alike in data_map[word]:
if word_alike.upper() in api:
return 1
return 0
df = pd.read_csv(src_name)
六一将发行葫芦兄弟邮票df["rel"] = df.apply(find_rel, axis=1)
<_csv(target_name, index=False, columns=["word", "API", "rel"])
python脚本要修改的地⽅，已标注成改改改tcl空调怎么调制热

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