diff --git a/AugmentText/augment_syntax/stanford_parse.py b/AugmentText/augment_syntax/stanford_parse.py
new file mode 100644
index 0000000..5605c98
--- /dev/null
+++ b/AugmentText/augment_syntax/stanford_parse.py
@@ -0,0 +1,163 @@
+# -*- coding: UTF-8 -*-
+# !/usr/bin/python
+# @time     :2019/4/9 22:49
+# @author   :Mo
+# @function :get main_part by stanfordcorenlp
+
+
+from conf.path_config import stanford_corenlp_full_path
+from stanfordcorenlp import StanfordCoreNLP
+
+
+# stanford-corenlp-full-2018-10-05需要预先下载，启动较慢
+nlp = StanfordCoreNLP(stanford_corenlp_full_path, lang='zh')
+
+
+def stanford_parse(sentence):
+    tokenize = nlp.word_tokenize(sentence)
+    pos_tag = nlp.pos_tag(sentence)
+    name_entity = nlp.ner(sentence)
+    syntax_tree = nlp.parse(sentence)
+    dependence = nlp.dependency_parse(sentence)
+
+    result_dict = {}
+    result_dict['tokenize'] = tokenize
+    result_dict['dependence'] = dependence
+    result_dict['parse'] = syntax_tree
+    return result_dict
+
+
+def combine_nn(tokenize, dependence, target):
+    """
+      合并名词短语等
+    :param dependence: dict, enhancedPlusPlusDependencies
+    :param target: str, subject or object 
+    :return: str, nn
+    """
+    if not target:
+        return target
+    else:
+        for dependence_one in dependence:
+            if target == tokenize[dependence_one[1]-1] if dependence_one[1]!=0 else "root" and dependence_one[0] == "nn":
+                target = tokenize[dependence_one[2]-1] + target
+                return target
+    return target
+
+
+def get_main_part_by_stanfordcorenlp(text):
+    """
+      根据依存句法生成句子
+    :param text: str, 输入
+    :return: str, result of syn sentence
+    """
+    # standcoreNLP 分词
+    result_dict = stanford_parse(text)
+    tokenize = result_dict['tokenize']
+    dependence = result_dict['dependence']
+    syntax_tree = result_dict['parse']
+    # 提取主谓宾
+    part_main = {"主": "", "谓": "", "宾": ""}
+    if len(syntax_tree) >= 2:
+        if "NP" in syntax_tree[1] or "ROOT" not in str(dependence):  # 名词短语 或者是没有谓语
+            count = 0
+            for syntax_tree_single in syntax_tree:
+                if "NP" in syntax_tree_single and "(" in syntax_tree_single and ")" in syntax_tree_single:
+                    token_np = syntax_tree_single.split(" ")[-1]
+                    token_np = token_np.replace("'", "").replace(")", "").strip()
+                    part_main["主"] = token_np if count == 0 else part_main["主"] + token_np
+                    count += 1
+            return part_main["主"] + part_main["谓"] + part_main["宾"]
+        else:
+            for dependence_one in dependence:
+                dep = dependence_one[0]
+                dep_dep_gloss = tokenize[dependence_one[2]-1]
+                if dep == "ROOT":  # ROOT作谓语
+                    part_main["谓"] = dep_dep_gloss
+                elif dep == "cop":  # 主系结构
+                    part_main["谓"] =  dep_dep_gloss + part_main["谓"]
+                else:  # 主语和宾语
+                    if dep == "nsubjpass" or dep == "dobj" or dep == "attr":
+                        part_main["宾"] = dep_dep_gloss
+                    elif dep == "nsubj" or dep == "top":
+                        part_main["主"] = dep_dep_gloss
+
+                part_main["主"] = combine_nn(tokenize, dependence, part_main["主"])
+                part_main["宾"] = combine_nn(tokenize, dependence, part_main["宾"])
+    return part_main["主"] + part_main["谓"] + part_main["宾"]
+
+
+if __name__ == "__main__":
+    sentence_list = [
+        "大漠帝国确实很喜欢JY",
+        "JY也喜欢大漠帝国哦！",
+        "这个工程的作者是momo",
+        "momo是一个无门无派的浪人",
+        "只有自信的程序员才能把握未来",
+        "主干识别可以提高检索系统的智能",
+        "打更的住在这里",
+        "人民的名义",
+        "名词短语",
+        "我一直很喜欢你",
+        "你被我喜欢",
+        "美丽又善良的你被卑微的我深深的喜欢着……",
+
+        "搜索momo可以找到我的博客",
+        "静安区体育局2013年部门决算情况说明",
+        "这类算法在有限的一段时间内终止",
+        "为什么在espace输入名字的部分拼音搜索不到相关的联系人？",
+        "espace安装失败报错",
+        "espace3.0无法批量导入",
+        "espace拼音模糊搜索",
+        "在运营数据中心内，ITSM系统的用户添加和更改了哪种类型的服务？",
+        "在ITSM系统中申请服务请求时，流转过程中，如何在当前环节转至同组其他人处理工单？",
+        "红旗飘",
+        "柳丝长",
+        "乐队奏国歌",
+        "红扑扑的朝霞露出了笑脸",
+        "初升的太阳照耀着峻峭的群山",
+        "一个农人在路上看见一条冻僵了的蛇",
+        "我打量了他一眼", ]
+    sentence_type = ["陈述句与否定句",
+            "秦耕真是一个聪明的孩子",
+            "衣服洗得不干净",
+            "他没有做完作业",
+            "他不敢不来",
+            "没有一个人不怕他",
+            "我非把这本书读完不可",
+            "同学们无不欢欣鼓舞",
+            "他妈妈不让他去,无非是怕他吃亏",
+            "想起一个人的旅途,不无寂寥之感",
+            "你未必不知道",
+            "各种问句",
+            "你可以那到100分, 是吗?",
+            "刚才接你的人是谁?",
+            "什么叫函数?",
+            "你爸爸怎么样了?",
+            "你每天几点休息?",
+            "你爸爸在哪儿?",
+            "我们是从广州走, 还是从成都走?",
+            "他是不是又迟到了?",
+            "难道他已经跑了?",
+            "我怎么能负这个责任呢?",
+            "你是来帮助我们的, 还是来拆我们的台的?",
+            "这些人甘愿当走狗, 你说可恨不可恨?",
+            "祈使句",
+             "快去捞饭!米烂了!",
+             "给我喝水, 我渴!",
+             "走哇, 妈妈!",
+             "不许动!",
+             "太好啦",
+    ]
+    for sen_one in sentence_list:
+        subject_object = get_main_part_by_stanfordcorenlp(sen_one)
+        print(sen_one + "   " + subject_object)
+
+    while True:
+        print("请输入sentence： ")
+        sen_test = input()
+        # syn_sentence_test = syn_by_syntactic_analys==(test_test)
+        syn_sentence_test = get_main_part_by_stanfordcorenlp(sen_test)
+        print(syn_sentence_test)
+
+    # Do not forget to close! The backend server will consume a lot memery
+    nlp.close()
\ No newline at end of file