V0.3

Author: blmoistawinde

README.md

@@ -16,7 +16,7 @@ HarvestText是一个基于少量种子词和背景知识完成一些领域自适
 ## 依赖
 - jieba
 - numpy, pandas
-- networkx(可选)
+- networkx
 
 ## 用法
 
@@ -258,11 +258,6 @@ from harvesttext import loadHT,saveHT
 para = "上港的武磊和恒大的郜林，谁是中国最好的前锋？那当然是武磊武球王了，他是射手榜第一，原来是弱点的单刀也有了进步"
 saveHT(ht,"ht_model1")
 ht2 = loadHT("ht_model1")
-print("cut with loaded model")
-print(ht2.seg(para))
-ht2.clear()
-print("cut with cleared model")
-print(ht2.seg(para))
 
 # 消除记录
 ht2.clear()

examples/ht_model1

@@ -8,6 +8,8 @@
 # 此资源被用于以下论文中:
 # Jun Li and Maosong Sun, Experimental Study on Sentiment Classification of Chinese Review using Machine Learning Techniques, in Proceding of IEEE NLPKE 2007
 # 李军 中文评论的褒贬义分类实验研究 硕士论文 清华大学 2008
+import os
+import json
 
 def get_qh_sent_dict():
     """
@@ -19,25 +21,20 @@ def get_qh_sent_dict():
     李军 中文评论的褒贬义分类实验研究 硕士论文 清华大学 2008
     :return: qh_sent_dict = {"pos":[words],"neg":[words]}
     """
-    qh_sent_dict = {"pos":[],"neg":[]}
-    with open("../resources/sentiment.dict.v1.0/tsinghua.positive.gb.txt","r") as f:
-        qh_sent_dict["pos"] = f.read().split()
 
-    with open("../resources/sentiment.dict.v1.0/tsinghua.negative.gb.txt","r") as f:
-        qh_sent_dict["neg"] = f.read().split()
+    pwd = os.path.abspath(os.path.dirname(__file__))
+    with open(pwd+"/resources/qh_sent_dict.json","r",encoding="utf-8") as f:
+        qh_sent_dict = json.load(f)
     return qh_sent_dict
 
 def get_sanguo():
     """
     获得三国演义原文
     :return: ["章节1文本","章节2文本",...]
     """
-    import os
-    basedir = "../resources/三国演义/"
-    docs = ["" for i in range(120)]
-    for i in range(1,121):
-        with open(basedir + f"{i}.txt", "r" ,encoding="utf-8") as f:
-            docs[i-1] = f.read().strip()
+    pwd = os.path.abspath(os.path.dirname(__file__))
+    with open(pwd+"/resources/sanguo_docs.json","r",encoding="utf-8") as f:
+        docs = json.load(f)
     return docs
 
 def get_sanguo_entity_dict():
@@ -47,7 +44,9 @@ def get_sanguo_entity_dict():
     :return: entity_mention_dict,entity_type_dict
     """
     import json
-    with open("../resources/sanguo_entity_dict.json","r",encoding="utf-8") as f:
+    pwd = os.path.abspath(os.path.dirname(__file__))
+    with open(pwd+"/resources/sanguo_entity_dict.json","r",encoding="utf-8") as f:
         entity_dict = json.load(f)
     return entity_dict["mention"], entity_dict["type"]
 
+