为了实现实际使用中有些简称的准确匹配,这个时候我们就需要定义一些同义词,具体做法就是在solr自带的synonyms.txt文件中填写我们想要的缩写与全称对应关系: 配置完成后需要重启solr 对应core managed-schema: java.nio.charset.MalformedInputException: Input length = 1错误 这是一个典型的编码错误,solr在加载相关配置时导致无法识别synonyms.txt 中的配置导致的,具体原因是默认下载到windows系统的文本编码格式是ANSI,解决方法就是把synonyms.txt文本的编码格式改成utf-8然后保存即可,查询结果如下:
机器之心也尝试使用 Synonyms 搜索一段中文的近义词,并有非常不错的反馈。 此外,Synonyms 的安装十分便捷,我们可以直接使用命令 pip install -U synonyms 完成。 ,因此 Synonyms 采用的词向量维度为 100。 用法 输出近义词向量: import synonyms print("人脸: %s" % (synonyms.nearby("人脸"))) print("识别: %s" % (synonyms.nearby 以友好的方式打印近义词,方便调试,display 调用了 synonyms#nearby 方法: >>> synonyms.display("飞机") '飞机'近义词: 1.
USER_SYNONYMS" ("SYNONYM_NAME", "TABLE_OWNER", "TABLE_NAME", " DB_LINK") AS select /*+ RULE */
同义词最好以文件的形式存储在config目录,配置updateable=true,synonyms_path GET my_synonyms/_settings GET my_synonyms/_mapping DELETE my_synonyms PUT my_synonyms { "settings": { "analysis": { "analyzer": { " } } } } POST my_synonyms/_close PUT my_synonyms/_settings { "analysis": { " "elk,elkb,elastic" ] } } } } POST my_synonyms/_open POST my_synonyms/_doc/1 { POST my_synonyms/_doc/3 { "content":"Elastic Stack is very powerful" } POST my_synonyms/_search {
创建同义词集示例PUT _synonyms/my-synonyms-set{ "synonyms_set": [ { "id": "rule-1", "synonyms": (results): synonyms_set = [{"id": slugify(product), "synonyms": synonyms} for product, synonyms in (results): synonyms_set = [{"id": slugify(product), "synonyms": synonyms} for product, synonyms in (id="products-synonyms-set", synonyms_set=synonyms_set) logging.info(json.dumps(response.body, 这个索引将使用synonyms_filter,应用之前创建的products-synonyms-set。
6、Elasticsearch 同义词 API 实操指南 6.1 创建同义词集 你可以用以下API请求创建一个新的同义词集: PUT _synonyms/my-synonyms-set { "synonyms_set ", "synonyms_set": "my-synonyms-set", "updateable": true } PUT _synonyms/my-synonyms-set { "synonyms_set": [ { "id": "pc", "synonyms": "pc => " } ] } 6.3.2 单个更新 或者,你也可以管理单个同义词规则: PUT _synonyms/my-synonyms-set/computer { "synonyms": /my-synonyms-set-v1 { "synonyms_set": [ { "id": "huawei", "synonyms": "huawei, yylx
(name=compression.type, value=producer, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms =segment.bytes, value=1073741824, source=STATIC_BROKER_CONFIG, isSensitive=false, isReadOnly=false, synonyms =message.format.version, value=2.5-IV0, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms name=file.delete.delay.ms, value=60000, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms (name=max.message.bytes, value=1048588, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms
题目 给你一个近义词表 synonyms 和一个句子 text , synonyms 表中是一些近义词对 ,你可以将句子 text 中每个单词用它的近义词来替换。 示例 1: 输入: synonyms = [["happy","joy"],["sad","sorrow"],["joy","cheerful"]], text = "I am happy today <= 10 synonyms[i].length == 2 synonyms[0] ! = synonyms[1] 所有单词仅包含英文字母,且长度最多为 10 。 text 最多包含 10 个单词,且单词间用单个空格分隔开。 , string text) { int i = 0; for(auto& s : synonyms) { if(!
"my_analyzer": { "tokenizer": "my_tokenizer", "filter": ["lowercase", "my_synonyms "my_tokenizer": { "type": "whitespace" } }, "filter": { "my_synonyms ": { "type": "synonym", "synonyms": [ "computer, pc", "laptop my_analyzer" } } }}在上述示例中,我们创建了一个名为“my_analyzer”的分析器,使用了自定义的“my_tokenizer”分词器和“lowercase”和“my_synonyms 此外,我们定义了一个名为“my_synonyms”的过滤器,将一些同义词(如“computer”和“pc”)转换为相同的单词。
-- in this example, we will only use synonyms at query time <filter class="solr.SynonymGraphFilterFactory " synonyms="index_synonyms.txt" ignoreCase="true" expand="false"/> <filter class="solr.FlattenGraphFilterFactory ignoreCase="true" words="stopwords.txt" /> <filter class="solr.SynonymGraphFilterFactory" synonyms ="synonyms.txt" ignoreCase="true" expand="true"/> <filter class="solr.LowerCaseFilterFactory
OUTPUT_DIR, exist_ok=True)# ===================== 工具函数 =====================def create_text_image(word, synonyms 0.25 - word_height/2 draw.text((x_word, y_word), word, font=word_font, fill="white") # 近义词 synonyms_text = ", ".join(synonyms) bbox_syn = draw.textbbox((0,0), synonyms_text, font=syn_font) syn_width , font=syn_font, fill="yellow") return imgdef generate_video_for_word(word, synonyms): # 生成语音 in words_dict.items(): generate_video_for_word(word, synonyms)print("所有视频生成完成!")
prefix_length": 0, "max_expansions": 50, "zero_terms_query": "NONE", "auto_generate_synonyms_phrase_query "lenient": false, "zero_terms_query": "NONE", "auto_generate_synonyms_phrase_query "lenient": false, "zero_terms_query": "NONE", "auto_generate_synonyms_phrase_query lenient": false, "zero_terms_query": "NONE", "auto_generate_synonyms_phrase_query lenient": false, "zero_terms_query": "NONE", "auto_generate_synonyms_phrase_query
------------- db_name string XM6320 SQL> select count(*) from dba_synonyms string KM3625 --下面的查询中仅有两个同义词,这两个同义是在创建DB的时候手动创建的,非使用datapump导入产生的 SQL> select count(*) from dba_synonyms logfile=exp_syns.log full=y \ > include=PUBLIC_SYNONYM/SYNONYM:\"IN \(SELECT synonym_name FROM dba_synonyms syns.dmp logfile=exp_syns.log full=y include=PUBLIC_SYNONYM/SYNONYM:"IN (SELECT synonym_name FROM dba_synonyms ------------ db_name string KM3625 SQL> select count(*) from dba_synonyms
<filter class="solr.LowerCaseFilterFactory"/> <filter class="solr.SynonymFilterFactory" synonyms ="synonyms.txt" ignoreCase="true" expand="true" /> </analyzer> <analyzer type="index"> <filter class="solr.LowerCaseFilterFactory"/> <filter class="solr.SynonymFilterFactory" synonyms ="synonyms.txt" ignoreCase="true" expand="true" /> </analyzer> </fieldType> 使用IKAnalyzer2012FF_u1 第三步 新建synonyms.txt文件,放在con目录下,保存同义词的字典格式如下 什么 => 啥 啥 => 什么 或者 什么,啥(逗号是英文格式下的) 注意:synonyms.txt文件写完必须另存为选择
select count(*)from dual * ERROR at line 1: ORA-01775: looping chain of synonyms select *from dual * ERROR at line 1: ORA-01775: looping chain of synonyms --如果没有尝试重启数据库的情况下 dual; select sysdate from dual * ERROR at line 1: ORA-01775: looping chain of synonyms Disconnection forced ORA-01775: looping chain of synonyms Process ID: 434 Session ID: 237 Serial number ORA-01775: looping chain of synonyms *** 2014-11-20 06:31:11.947 USER (ospid: 434): terminating the
使用的链接在这里:哈工大同义词林扩展版 使用代码编写时也可以利用Python的Synonyms库来获取同义词。 其已经开源,链接为:synonyms 如: import synonyms print("人脸: %s" % (synonyms.nearby("人脸"))) print("识别: %s" % (synonyms.nearby
示例: 输入:names = ["John(15)","Jon(12)","Chris(13)","Kris(4)","Christopher(19)"], synonyms = ["(Jon,John string,int> m;//名称,频次 public: vector<string> trulyMostPopular(vector<string>& names, vector<string>& synonyms [name] = count;//获取每个名字的次数 father[name] = name;//并查集初始化 } for(auto& n : synonyms [name1] = name1;//并查集初始化 father[name2] = name2;//并查集初始化 } for(auto& n : synonyms
英文指令: Rephrase this passage by restructuring the sentences, adjusting word counts, and substituting synonyms original text by adjusting word order, increasing or decreasing the number of words, and substituting synonyms 英文指令: First, rearrange the sentences, modify the wording by adding or removing terms, and employ synonyms 英文指令: Replace key terms in the text with appropriate synonyms to lower repetition and enhance the originality
英文指令: "Rephrase this passage by adjusting the word order, modifying the length, and substituting synonyms text by adjusting the order of words, increasing or decreasing the number of words, and substituting synonyms 英文指令: "Begin by adjusting the sentence order, increasing or decreasing word count, and substituting synonyms 英文指令: "Kindly replace key terms in this section with appropriate synonyms to reduce similarity and enhance 英文指令: "Replace key terms in the text with suitable synonyms to lower the similarity index and enhance
Dictionary new_words = ['奥预赛', '折叠屏'] # 新词 stopwords = {' ', '再', '的', '们', '为', '时', ':'} # 停用词 synonyms remove_stopwords(ls): # 去除停用词 return [word for word in ls if word not in stopwords] def replace_synonyms (ls): # 替换同义词 return [synonyms[i] if i in synonyms else i for i in ls] documents = [ '足协申请取消女足奥预赛韩国主场比赛 '今晚视频直播华为新品发布会:全新折叠屏手机亮相'] add_new_words() words_ls = [] for text in documents: words = replace_synonyms