{"id":477061,"date":"2023-08-09T09:06:59","date_gmt":"2023-08-09T09:06:59","guid":{"rendered":""},"modified":"2023-09-05T11:13:56","modified_gmt":"2023-09-05T11:13:56","slug":"elmo","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/jp\/wiki\/elmo\/","title":{"rendered":"\u30a8\u30eb\u30e2"},"content":{"rendered":"<p>ELMo \u306f\u3001Embeddings from Language Models \u306e\u7565\u3067\u3001\u753b\u671f\u7684\u306a\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u30d9\u30fc\u30b9\u306e\u8a00\u8a9e\u8868\u73fe\u30e2\u30c7\u30eb\u3067\u3059\u30022018 \u5e74\u306b Allen Institute for Artificial Intelligence (AI2) \u306e\u7814\u7a76\u8005\u306b\u3088\u3063\u3066\u958b\u767a\u3055\u308c\u305f ELMo \u306f\u3001\u81ea\u7136\u8a00\u8a9e\u51e6\u7406 (NLP) \u30bf\u30b9\u30af\u306b\u9769\u547d\u3092\u3082\u305f\u3089\u3057\u3001OneProxy \u306a\u3069\u306e\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc \u30d7\u30ed\u30d0\u30a4\u30c0\u30fc\u3092\u542b\u3080\u3055\u307e\u3056\u307e\u306a\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u5f37\u5316\u3057\u307e\u3057\u305f\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001ELMo \u306e\u6b74\u53f2\u3001\u5185\u90e8\u306e\u4ed5\u7d44\u307f\u3001\u4e3b\u306a\u6a5f\u80fd\u3001\u7a2e\u985e\u3001\u4f7f\u7528\u4f8b\u3001\u5c06\u6765\u306e\u5c55\u671b\u3001\u304a\u3088\u3073\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3068\u306e\u6f5c\u5728\u7684\u306a\u95a2\u9023\u6027\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<h2>ELMo\u306e\u8d77\u6e90\u3068\u305d\u306e\u6700\u521d\u306e\u8a00\u53ca\u306e\u6b74\u53f2<\/h2>\n<p>ELMo \u306e\u8d77\u6e90\u306f\u3001\u3088\u308a\u6587\u8108\u3092\u8003\u616e\u3057\u305f\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u306e\u5fc5\u8981\u6027\u306b\u9061\u308a\u307e\u3059\u3002Word2Vec \u3084 GloVe \u306a\u3069\u306e\u5f93\u6765\u306e\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3067\u306f\u3001\u5404\u5358\u8a9e\u3092\u5468\u56f2\u306e\u6587\u8108\u3092\u7121\u8996\u3057\u3066\u72ec\u7acb\u3057\u305f\u30a8\u30f3\u30c6\u30a3\u30c6\u30a3\u3068\u3057\u3066\u6271\u3063\u3066\u3044\u307e\u3057\u305f\u3002\u3057\u304b\u3057\u3001\u7814\u7a76\u8005\u306f\u3001\u5358\u8a9e\u306e\u610f\u5473\u306f\u6587\u4e2d\u306e\u6587\u8108\u306b\u3088\u3063\u3066\u5927\u304d\u304f\u5909\u308f\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u3092\u767a\u898b\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>ELMo \u304c\u521d\u3081\u3066\u8a00\u53ca\u3055\u308c\u305f\u306e\u306f\u30012018 \u5e74\u306b Matthew Peters \u3089\u304c\u767a\u8868\u3057\u305f\u300cDeep contextualized word representations\u300d\u3068\u3044\u3046\u8ad6\u6587\u3067\u3059\u3002\u3053\u306e\u8ad6\u6587\u3067\u306f\u3001\u53cc\u65b9\u5411\u8a00\u8a9e\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u4f9d\u5b58\u306e\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3092\u751f\u6210\u3059\u308b\u65b0\u3057\u3044\u30a2\u30d7\u30ed\u30fc\u30c1\u3068\u3057\u3066 ELMo \u304c\u7d39\u4ecb\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n<h2>ELMo \u306b\u95a2\u3059\u308b\u8a73\u7d30\u60c5\u5831\u3002\u30c8\u30d4\u30c3\u30af ELMo \u306e\u62e1\u5f35\u3002<\/h2>\n<p>ELMo \u306f\u3001\u53cc\u65b9\u5411\u8a00\u8a9e\u30e2\u30c7\u30eb\u306e\u529b\u3092\u6d3b\u7528\u3057\u3066\u3001\u6587\u8108\u306b\u5373\u3057\u305f\u5358\u8a9e\u8868\u73fe\u65b9\u6cd5\u3092\u63a1\u7528\u3057\u3066\u3044\u307e\u3059\u3002LSTM (Long Short-Term Memory) \u306a\u3069\u306e\u5f93\u6765\u306e\u8a00\u8a9e\u30e2\u30c7\u30eb\u306f\u3001\u904e\u53bb\u306e\u5358\u8a9e\u306e\u4f9d\u5b58\u95a2\u4fc2\u3092\u6349\u3048\u306a\u304c\u3089\u3001\u6587\u3092\u5de6\u304b\u3089\u53f3\u306b\u51e6\u7406\u3057\u307e\u3059\u3002\u5bfe\u7167\u7684\u306b\u3001ELMo \u306f\u524d\u65b9 LSTM \u3068\u5f8c\u65b9 LSTM \u306e\u4e21\u65b9\u3092\u7d44\u307f\u8fbc\u3093\u3067\u304a\u308a\u3001\u30e2\u30c7\u30eb\u306f\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3092\u4f5c\u6210\u3059\u308b\u969b\u306b\u6587\u5168\u4f53\u306e\u6587\u8108\u3092\u8003\u616e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>ELMo \u306e\u5f37\u307f\u306f\u3001\u5468\u56f2\u306e\u5358\u8a9e\u306b\u57fa\u3065\u3044\u3066\u5404\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e\u52d5\u7684\u306a\u5358\u8a9e\u8868\u73fe\u3092\u751f\u6210\u3067\u304d\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u5358\u8a9e\u304c\u6587\u8108\u306b\u5fdc\u3058\u3066\u8907\u6570\u306e\u610f\u5473\u3092\u6301\u3064\u53ef\u80fd\u6027\u304c\u3042\u308b\u591a\u7fa9\u6027\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3057\u307e\u3059\u3002\u6587\u8108\u4f9d\u5b58\u306e\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3092\u5b66\u7fd2\u3059\u308b\u3053\u3068\u3067\u3001ELMo \u306f\u611f\u60c5\u5206\u6790\u3001\u56fa\u6709\u8868\u73fe\u8a8d\u8b58\u3001\u54c1\u8a5e\u30bf\u30b0\u4ed8\u3051\u306a\u3069\u306e\u3055\u307e\u3056\u307e\u306a NLP \u30bf\u30b9\u30af\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5927\u5e45\u306b\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002<\/p>\n<h2>ELMo \u306e\u5185\u90e8\u69cb\u9020\u3002ELMo \u306e\u4ed5\u7d44\u307f\u3002<\/h2>\n<p>ELMo \u306e\u5185\u90e8\u69cb\u9020\u306f\u3001\u6df1\u5c64\u53cc\u65b9\u5411\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u3044\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u30012 \u3064\u306e\u4e3b\u8981\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<ol>\n<li>\n<p><strong>\u6587\u5b57\u30d9\u30fc\u30b9\u306e\u5358\u8a9e\u8868\u73fe:<\/strong> ELMo \u306f\u307e\u305a\u3001\u6587\u5b57\u30ec\u30d9\u30eb\u306e CNN (\u7573\u307f\u8fbc\u307f\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af) \u3092\u4f7f\u7528\u3057\u3066\u5404\u5358\u8a9e\u3092\u6587\u5b57\u30d9\u30fc\u30b9\u306e\u8868\u73fe\u306b\u5909\u63db\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30e2\u30c7\u30eb\u306f\u8a9e\u5f59\u5916 (OOV) \u306e\u5358\u8a9e\u3092\u51e6\u7406\u3057\u3001\u30b5\u30d6\u30ef\u30fc\u30c9\u60c5\u5831\u3092\u52b9\u679c\u7684\u306b\u30ad\u30e3\u30d7\u30c1\u30e3\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u53cc\u65b9\u5411 LSTM:<\/strong> \u6587\u5b57\u30d9\u30fc\u30b9\u306e\u5358\u8a9e\u8868\u73fe\u3092\u53d6\u5f97\u3057\u305f\u5f8c\u3001ELMo \u306f\u305d\u308c\u3092\u53cc\u65b9\u5411 LSTM \u306e 2 \u3064\u306e\u5c64\u306b\u5165\u529b\u3057\u307e\u3059\u3002\u6700\u521d\u306e LSTM \u306f\u6587\u3092\u5de6\u304b\u3089\u53f3\u306b\u51e6\u7406\u3057\u30012 \u756a\u76ee\u306e LSTM \u306f\u6587\u3092\u53f3\u304b\u3089\u5de6\u306b\u51e6\u7406\u3057\u307e\u3059\u3002\u4e21\u65b9\u306e LSTM \u306e\u96a0\u3057\u72b6\u614b\u304c\u9023\u7d50\u3055\u308c\u3001\u6700\u7d42\u7684\u306a\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u7d50\u679c\u3068\u3057\u3066\u5f97\u3089\u308c\u308b\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u5316\u3055\u308c\u305f\u57cb\u3081\u8fbc\u307f\u306f\u3001\u4e0b\u6d41\u306e NLP \u30bf\u30b9\u30af\u306e\u5165\u529b\u3068\u3057\u3066\u4f7f\u7528\u3055\u308c\u3001\u5f93\u6765\u306e\u9759\u7684\u306a\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3068\u6bd4\u8f03\u3057\u3066\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u5927\u5e45\u306b\u5411\u4e0a\u3057\u307e\u3059\u3002<\/p>\n<h2>ELMo \u306e\u4e3b\u306a\u6a5f\u80fd\u306e\u5206\u6790\u3002<\/h2>\n<p>ELMo \u306f\u3001\u5f93\u6765\u306e\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3068\u306f\u7570\u306a\u308b\u3044\u304f\u3064\u304b\u306e\u91cd\u8981\u306a\u6a5f\u80fd\u3092\u8a87\u308a\u307e\u3059\u3002<\/p>\n<ol>\n<li>\n<p><strong>\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u306e\u611f\u5ea6:<\/strong> ELMo \u306f\u5358\u8a9e\u306e\u6587\u8108\u60c5\u5831\u3092\u30ad\u30e3\u30d7\u30c1\u30e3\u3057\u3001\u3088\u308a\u6b63\u78ba\u3067\u610f\u5473\u306e\u3042\u308b\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3092\u5b9f\u73fe\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u7fa9\u6027\u306e\u51e6\u7406:<\/strong> ELMo \u306f\u3001\u6587\u5168\u4f53\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u8003\u616e\u3059\u308b\u3053\u3068\u3067\u3001\u9759\u7684\u57cb\u3081\u8fbc\u307f\u306e\u5236\u9650\u3092\u514b\u670d\u3057\u3001\u591a\u7fa9\u8a9e\u306e\u8907\u6570\u306e\u610f\u5473\u3092\u51e6\u7406\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8a9e\u5f59\u5916\uff08OOV\uff09\u30b5\u30dd\u30fc\u30c8:<\/strong> ELMo \u306e\u6587\u5b57\u30d9\u30fc\u30b9\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u3088\u308a\u3001OOV \u5358\u8a9e\u3092\u52b9\u679c\u7684\u306b\u51e6\u7406\u3057\u3001\u5b9f\u969b\u306e\u30b7\u30ca\u30ea\u30aa\u3067\u306e\u5805\u7262\u6027\u3092\u78ba\u4fdd\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8ee2\u79fb\u5b66\u7fd2:<\/strong> \u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307f\u306e ELMo \u30e2\u30c7\u30eb\u306f\u3001\u7279\u5b9a\u306e\u4e0b\u6d41\u30bf\u30b9\u30af\u306b\u5408\u308f\u305b\u3066\u5fae\u8abf\u6574\u3067\u304d\u308b\u305f\u3081\u3001\u52b9\u7387\u7684\u306a\u8ee2\u79fb\u5b66\u7fd2\u304c\u53ef\u80fd\u306b\u306a\u308a\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6642\u9593\u304c\u77ed\u7e2e\u3055\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6700\u5148\u7aef\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9:<\/strong> ELMo \u306f\u3001\u3055\u307e\u3056\u307e\u306a NLP \u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3067\u6700\u5148\u7aef\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5b9f\u8a3c\u3057\u3001\u305d\u306e\u6c4e\u7528\u6027\u3068\u6709\u52b9\u6027\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u3069\u306e\u3088\u3046\u306a\u7a2e\u985e\u306e ELMo \u304c\u5b58\u5728\u3059\u308b\u304b\u3092\u66f8\u304d\u307e\u3059\u3002\u8868\u3068\u30ea\u30b9\u30c8\u3092\u4f7f\u7528\u3057\u3066\u66f8\u304d\u307e\u3059\u3002<\/h2>\n<p>ELMo \u30e2\u30c7\u30eb\u306b\u306f\u3001\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u8868\u73fe\u306b\u57fa\u3065\u3044\u3066\u4e3b\u306b 2 \u3064\u306e\u30bf\u30a4\u30d7\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th>\u30bf\u30a4\u30d7<\/th>\n<th>\u8aac\u660e<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u30aa\u30ea\u30b8\u30ca\u30ebELMo<\/td>\n<td>\u3053\u306e\u30e2\u30c7\u30eb\u306f\u3001\u53cc\u65b9\u5411 LSTM \u306b\u57fa\u3065\u3044\u3066\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u4f9d\u5b58\u306e\u5358\u8a9e\u57cb\u3081\u8fbc\u307f\u3092\u751f\u6210\u3057\u307e\u3059\u3002\u6587\u5168\u4f53\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u306b\u57fa\u3065\u3044\u3066\u5358\u8a9e\u8868\u73fe\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u30a8\u30eb\u30e22.0<\/td>\n<td>\u3053\u306e\u30e2\u30c7\u30eb\u306f\u30aa\u30ea\u30b8\u30ca\u30eb\u306e ELMo \u3092\u57fa\u306b\u69cb\u7bc9\u3055\u308c\u3066\u304a\u308a\u3001\u53cc\u65b9\u5411 LSTM \u306b\u52a0\u3048\u3066\u81ea\u5df1\u6ce8\u610f\u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u7d44\u307f\u8fbc\u3093\u3067\u3044\u307e\u3059\u3002\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u57cb\u3081\u8fbc\u307f\u3092\u3055\u3089\u306b\u6539\u826f\u3057\u3001\u7279\u5b9a\u306e\u30bf\u30b9\u30af\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>ELMo \u306e\u4f7f\u3044\u65b9\u3001\u4f7f\u7528\u6642\u306b\u767a\u751f\u3059\u308b\u554f\u984c\u3068\u305d\u306e\u89e3\u6c7a\u7b56\u3002<\/h2>\n<p>ELMo \u306f\u3001\u6b21\u306e\u3088\u3046\u306a\u3055\u307e\u3056\u307e\u306a NLP \u30bf\u30b9\u30af\u306b\u5fdc\u7528\u3067\u304d\u307e\u3059\u3002<\/p>\n<ol>\n<li>\n<p><strong>\u611f\u60c5\u5206\u6790\uff1a<\/strong> ELMo \u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u5316\u3055\u308c\u305f\u57cb\u3081\u8fbc\u307f\u306f\u3001\u5fae\u5999\u306a\u611f\u60c5\u3084\u6c17\u6301\u3061\u3092\u6349\u3048\u308b\u306e\u306b\u5f79\u7acb\u3061\u3001\u3088\u308a\u6b63\u78ba\u306a\u611f\u60c5\u5206\u6790\u30e2\u30c7\u30eb\u306b\u3064\u306a\u304c\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u56fa\u6709\u8868\u73fe\u8a8d\u8b58 (NER):<\/strong> NER \u30b7\u30b9\u30c6\u30e0\u306f\u3001\u5468\u56f2\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u306b\u57fa\u3065\u3044\u3066\u30a8\u30f3\u30c6\u30a3\u30c6\u30a3\u306e\u8a00\u53ca\u3092\u660e\u78ba\u306b\u3059\u308b ELMo \u306e\u6a5f\u80fd\u306e\u6069\u6075\u3092\u53d7\u3051\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8cea\u554f\u3078\u306e\u56de\u7b54:<\/strong> ELMo \u306f\u8cea\u554f\u3084\u6587\u7ae0\u306e\u6587\u8108\u3092\u7406\u89e3\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u3001\u8cea\u554f\u5fdc\u7b54\u30b7\u30b9\u30c6\u30e0\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6a5f\u68b0\u7ffb\u8a33:<\/strong> ELMo \u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u8a8d\u8b58\u578b\u5358\u8a9e\u8868\u73fe\u306f\u3001\u6a5f\u68b0\u7ffb\u8a33\u30e2\u30c7\u30eb\u306e\u7ffb\u8a33\u54c1\u8cea\u3092\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u305f\u3060\u3057\u3001ELMo \u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u3044\u304f\u3064\u304b\u306e\u8ab2\u984c\u304c\u751f\u3058\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li>\n<p><strong>\u8a08\u7b97\u30b3\u30b9\u30c8\u304c\u9ad8\u3044:<\/strong> ELMo \u306f\u3001\u305d\u306e\u6df1\u3044\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3068\u53cc\u65b9\u5411\u51e6\u7406\u306e\u305f\u3081\u306b\u3001\u304b\u306a\u308a\u306e\u8a08\u7b97\u30ea\u30bd\u30fc\u30b9\u3092\u5fc5\u8981\u3068\u3057\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u30ea\u30bd\u30fc\u30b9\u304c\u5236\u9650\u3055\u308c\u305f\u74b0\u5883\u3067\u306f\u8ab2\u984c\u3068\u306a\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u63a8\u8ad6\u6642\u9593\u304c\u9577\u3044:<\/strong> ELMo \u57cb\u3081\u8fbc\u307f\u306e\u751f\u6210\u306b\u306f\u6642\u9593\u304c\u304b\u304b\u308a\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0 \u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7d71\u5408\u306e\u8907\u96d1\u3055:<\/strong> ELMo \u3092\u65e2\u5b58\u306e NLP \u30d1\u30a4\u30d7\u30e9\u30a4\u30f3\u306b\u7d44\u307f\u8fbc\u3080\u306b\u306f\u3001\u8ffd\u52a0\u306e\u52b4\u529b\u3068\u9069\u5fdc\u304c\u5fc5\u8981\u306b\u306a\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u3053\u308c\u3089\u306e\u8ab2\u984c\u3092\u8efd\u6e1b\u3059\u308b\u305f\u3081\u306b\u3001\u7814\u7a76\u8005\u3084\u5b9f\u8df5\u8005\u306f\u3001ELMo \u3092\u3088\u308a\u30a2\u30af\u30bb\u30b9\u3057\u3084\u3059\u304f\u52b9\u7387\u7684\u306b\u3059\u308b\u305f\u3081\u306e\u6700\u9069\u5316\u624b\u6cd5\u3001\u30e2\u30c7\u30eb\u84b8\u7559\u3001\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2 \u30a2\u30af\u30bb\u30e9\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u691c\u8a0e\u3057\u3066\u304d\u307e\u3057\u305f\u3002<\/p>\n<h2>\u4e3b\u306a\u7279\u5fb4\u3084\u305d\u306e\u4ed6\u306e\u985e\u4f3c\u7528\u8a9e\u3068\u306e\u6bd4\u8f03\u3092\u8868\u3084\u30ea\u30b9\u30c8\u306e\u5f62\u5f0f\u3067\u793a\u3057\u307e\u3059\u3002<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u6027<\/th>\n<th>\u30a8\u30eb\u30e2<\/th>\n<th>\u30ef\u30fc\u30c92\u30d9\u30af\u30c8\u30eb<\/th>\n<th>\u30b0\u30ed\u30fc\u30d6<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u30bb\u30f3\u30b7\u30c6\u30a3\u30d3\u30c6\u30a3<\/td>\n<td>\u306f\u3044<\/td>\n<td>\u3044\u3044\u3048<\/td>\n<td>\u3044\u3044\u3048<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u7fa9\u6027\u306e\u6271\u3044<\/td>\n<td>\u306f\u3044<\/td>\n<td>\u3044\u3044\u3048<\/td>\n<td>\u3044\u3044\u3048<\/td>\n<\/tr>\n<tr>\n<td>\u8a9e\u5f59\u5916\uff08OOV\uff09<\/td>\n<td>\u7d20\u6674\u3089\u3057\u3044<\/td>\n<td>\u9650\u5b9a<\/td>\n<td>\u9650\u5b9a<\/td>\n<\/tr>\n<tr>\n<td>\u8ee2\u79fb\u5b66\u7fd2<\/td>\n<td>\u306f\u3044<\/td>\n<td>\u306f\u3044<\/td>\n<td>\u306f\u3044<\/td>\n<\/tr>\n<tr>\n<td>\u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u306e\u30b5\u30a4\u30ba<\/td>\n<td>\u5927\u304d\u3044<\/td>\n<td>\u4e2d\u304f\u3089\u3044<\/td>\n<td>\u5927\u304d\u3044<\/td>\n<\/tr>\n<tr>\n<td>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306e\u6642\u9593<\/td>\n<td>\u9ad8\u3044<\/td>\n<td>\u4f4e\u3044<\/td>\n<td>\u4f4e\u3044<\/td>\n<\/tr>\n<tr>\n<td>\u30e2\u30c7\u30eb\u30b5\u30a4\u30ba<\/td>\n<td>\u5927\u304d\u3044<\/td>\n<td>\u5c0f\u3055\u3044<\/td>\n<td>\u4e2d\u304f\u3089\u3044<\/td>\n<\/tr>\n<tr>\n<td>NLP\u30bf\u30b9\u30af\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9<\/td>\n<td>\u6700\u5148\u7aef\u306e<\/td>\n<td>\u9069\u5ea6<\/td>\n<td>\u826f\u3044<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>ELMo\u306b\u95a2\u9023\u3059\u308b\u5c06\u6765\u306e\u5c55\u671b\u3068\u6280\u8853\u3002<\/h2>\n<p>\u6025\u901f\u306b\u9032\u5316\u3059\u308b\u3042\u3089\u3086\u308b\u5206\u91ce\u3068\u540c\u69d8\u306b\u3001ELMo \u306e\u5c06\u6765\u306b\u306f\u6709\u671b\u306a\u9032\u6b69\u304c\u671f\u5f85\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u6f5c\u5728\u7684\u306a\u767a\u5c55\u306b\u306f\u6b21\u306e\u3088\u3046\u306a\u3082\u306e\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li>\n<p><strong>\u52b9\u7387\u6027\u306e\u5411\u4e0a:<\/strong> \u7814\u7a76\u8005\u306f\u3001ELMo \u306e\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3092\u6700\u9069\u5316\u3057\u3066\u8a08\u7b97\u30b3\u30b9\u30c8\u3068\u63a8\u8ad6\u6642\u9593\u3092\u524a\u6e1b\u3057\u3001\u3088\u308a\u5e45\u5e83\u3044\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3067\u5229\u7528\u3057\u3084\u3059\u304f\u3059\u308b\u3053\u3068\u306b\u91cd\u70b9\u3092\u7f6e\u304f\u3068\u601d\u308f\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u8a00\u8a9e\u30b5\u30dd\u30fc\u30c8:<\/strong> ELMo \u306e\u6a5f\u80fd\u3092\u62e1\u5f35\u3057\u3066\u8907\u6570\u306e\u8a00\u8a9e\u3092\u51e6\u7406\u3067\u304d\u308b\u3088\u3046\u306b\u3059\u308b\u3053\u3068\u3067\u3001\u8a00\u8a9e\u9593 NLP \u30bf\u30b9\u30af\u306e\u65b0\u305f\u306a\u53ef\u80fd\u6027\u304c\u958b\u304b\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7d99\u7d9a\u7684\u306a\u5b66\u7fd2:<\/strong> \u7d99\u7d9a\u7684\u5b66\u7fd2\u6280\u8853\u306e\u9032\u6b69\u306b\u3088\u308a\u3001ELMo \u306f\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u306b\u6bb5\u968e\u7684\u306b\u9069\u5fdc\u3057\u3066\u5b66\u7fd2\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u3001\u9032\u5316\u3059\u308b\u8a00\u8a9e\u30d1\u30bf\u30fc\u30f3\u306b\u5e38\u306b\u6700\u65b0\u306e\u72b6\u614b\u3092\u4fdd\u3064\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u30e2\u30c7\u30eb\u5727\u7e2e:<\/strong> \u30e2\u30c7\u30eb\u306e\u84b8\u7559\u3084\u91cf\u5b50\u5316\u306a\u3069\u306e\u6280\u8853\u3092\u9069\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u3042\u307e\u308a\u72a0\u7272\u306b\u3059\u308b\u3053\u3068\u306a\u304f\u3001ELMo \u306e\u8efd\u91cf\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ul>\n<h2>\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3092 ELMo \u3067\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3001\u307e\u305f\u306f ELMo \u306b\u95a2\u9023\u4ed8\u3051\u308b\u65b9\u6cd5\u3002<\/h2>\n<p>\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001ELMo \u304b\u3089\u3055\u307e\u3056\u307e\u306a\u30e1\u30ea\u30c3\u30c8\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<ol>\n<li>\n<p><strong>\u5f37\u5316\u3055\u308c\u305f\u30b3\u30f3\u30c6\u30f3\u30c4 \u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0:<\/strong> ELMo \u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u57cb\u3081\u8fbc\u307f\u306b\u3088\u308a\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3067\u4f7f\u7528\u3055\u308c\u308b\u30b3\u30f3\u30c6\u30f3\u30c4 \u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0 \u30b7\u30b9\u30c6\u30e0\u306e\u7cbe\u5ea6\u304c\u5411\u4e0a\u3057\u3001\u4e0d\u9069\u5207\u307e\u305f\u306f\u6709\u5bb3\u306a\u30b3\u30f3\u30c6\u30f3\u30c4\u3092\u3088\u308a\u9069\u5207\u306b\u8b58\u5225\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8a00\u8a9e\u8a8d\u8b58\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0:<\/strong> ELMo \u306f\u8a00\u8a9e\u8a8d\u8b58\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u3092\u652f\u63f4\u3057\u3001\u30e6\u30fc\u30b6\u30fc \u30ea\u30af\u30a8\u30b9\u30c8\u304c\u6700\u3082\u9069\u5207\u306a\u8a00\u8a9e\u51e6\u7406\u6a5f\u80fd\u3092\u5099\u3048\u305f\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306b\u9001\u4fe1\u3055\u308c\u308b\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7570\u5e38\u691c\u51fa:<\/strong> ELMo \u3092\u4f7f\u7528\u3057\u3066\u30e6\u30fc\u30b6\u30fc\u306e\u884c\u52d5\u3068\u8a00\u8a9e\u30d1\u30bf\u30fc\u30f3\u3092\u5206\u6790\u3059\u308b\u3053\u3068\u3067\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u7591\u308f\u3057\u3044\u30a2\u30af\u30c6\u30a3\u30d3\u30c6\u30a3\u3092\u3088\u308a\u9069\u5207\u306b\u691c\u51fa\u3057\u3001\u9632\u6b62\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u8a00\u8a9e\u30d7\u30ed\u30ad\u30b7:<\/strong> ELMo \u306e\u591a\u8a00\u8a9e\u30b5\u30dd\u30fc\u30c8 (\u5c06\u6765\u5229\u7528\u53ef\u80fd\u306b\u306a\u3063\u305f\u5834\u5408) \u306b\u3088\u308a\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3055\u307e\u3056\u307e\u306a\u8a00\u8a9e\u306e\u30b3\u30f3\u30c6\u30f3\u30c4\u3092\u3088\u308a\u52b9\u7387\u7684\u306b\u51e6\u7406\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u5168\u4f53\u3068\u3057\u3066\u3001ELMo \u3092\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc \u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3\u306b\u7d71\u5408\u3059\u308b\u3068\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u5411\u4e0a\u3057\u3001\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u304c\u5f37\u5316\u3055\u308c\u3001\u30e6\u30fc\u30b6\u30fc \u30a8\u30af\u30b9\u30da\u30ea\u30a8\u30f3\u30b9\u304c\u3088\u308a\u30b7\u30fc\u30e0\u30ec\u30b9\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<h2>\u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n<p>ELMo \u3068\u305d\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u8a73\u7d30\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u306e\u30ea\u30bd\u30fc\u30b9\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/allennlp.org\/elmo\" target=\"_new\" rel=\"noopener nofollow\">ELMo: \u8a00\u8a9e\u30e2\u30c7\u30eb\u304b\u3089\u306e\u57cb\u3081\u8fbc\u307f<\/a><\/li>\n<li><a href=\"https:\/\/www.aclweb.org\/anthology\/N18-1202.pdf\" target=\"_new\" rel=\"noopener nofollow\">ELMo\u30aa\u30ea\u30b8\u30ca\u30eb\u8ad6\u6587<\/a><\/li>\n<li><a href=\"https:\/\/www.aclweb.org\/anthology\/P19-1613.pdf\" target=\"_new\" rel=\"noopener nofollow\">ELMo 2.0: \u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304c\u4e0d\u8db3\u3057\u3066\u3044\u308b<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/allenai\/allennlp\/blob\/main\/tutorials\/how_to\/elmo.md\" target=\"_new\" rel=\"noopener nofollow\">AI2\u306b\u3088\u308bELMo\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468299,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477061","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>ELMo: Empowering Language Models for Proxy Server Providers<\/mark>","faq_items":[{"question":"What is ELMo?","answer":"<p>ELMo, short for Embeddings from Language Models, is a deep learning-based language representation model developed by the Allen Institute for Artificial Intelligence (AI2) in 2018. It generates context-sensitive word embeddings by using bidirectional language models, revolutionizing various natural language processing (NLP) tasks.<\/p>"},{"question":"How does ELMo work?","answer":"<p>ELMo utilizes a deep bidirectional language model with character-based word representations and bidirectional LSTMs. It processes sentences from both left to right and right to left, capturing the entire context of words. The resulting contextualized embeddings are used for downstream NLP tasks, enhancing their performance significantly.<\/p>"},{"question":"What are the key features of ELMo?","answer":"<p>ELMo's key features include context sensitivity, polysemy handling, out-of-vocabulary (OOV) support, transfer learning, and state-of-the-art performance on NLP tasks. Its contextual embeddings enable more accurate word representations based on sentence context, making it highly versatile and effective.<\/p>"},{"question":"What types of ELMo models exist?","answer":"<p>There are two main types of ELMo models:<\/p><ol><li><p>Original ELMo: This model generates context-sensitive word embeddings based on bidirectional LSTMs, providing word representations based on the entire sentence context.<\/p><\/li><li><p>ELMo 2.0: Building upon the original ELMo, this model incorporates self-attention mechanisms in addition to bidirectional LSTMs, further refining contextual embeddings for improved performance.<\/p><\/li><\/ol>"},{"question":"How can ELMo be used?","answer":"<p>ELMo finds applications in various NLP tasks such as sentiment analysis, named entity recognition, question answering, and machine translation. Its context-aware word representations enhance the performance of these tasks by capturing nuanced meanings and emotions.<\/p>"},{"question":"What challenges are associated with using ELMo?","answer":"<p>Using ELMo may present challenges such as high computational cost, long inference time, and integration complexity. However, researchers have explored optimization techniques, model distillation, and hardware acceleration to mitigate these issues.<\/p>"},{"question":"What are the future perspectives for ELMo?","answer":"<p>The future of ELMo holds promising advancements, including efficiency improvements, multilingual support, continual learning, and model compression. These developments will further enhance ELMo's capabilities and accessibility in the evolving field of NLP.<\/p>"},{"question":"How can proxy servers benefit from ELMo?","answer":"<p>Proxy servers can benefit from ELMo through enhanced content filtering, language-aware routing, anomaly detection, and multilingual proxying. ELMo's contextual embeddings enable better identification of inappropriate content and improved user experience.<\/p>"},{"question":"Where can I find more information about ELMo?","answer":"<p>For more information about ELMo and its applications, you can refer to the following resources:<\/p><ol><li>ELMo: Embeddings from Language Models (<a href=\"https:\/\/allennlp.org\/elmo\" target=\"_new\">https:\/\/allennlp.org\/elmo<\/a>)<\/li><li>Original ELMo paper (<a href=\"https:\/\/www.aclweb.org\/anthology\/N18-1202.pdf\" target=\"_new\">https:\/\/www.aclweb.org\/anthology\/N18-1202.pdf<\/a>)<\/li><li>ELMo 2.0: Missing Pretraining (<a href=\"https:\/\/www.aclweb.org\/anthology\/P19-1613.pdf\" target=\"_new\">https:\/\/www.aclweb.org\/anthology\/P19-1613.pdf<\/a>)<\/li><li>Tutorial on ELMo by AI2 (<a href=\"https:\/\/github.com\/allenai\/allennlp\/blob\/main\/tutorials\/how_to\/elmo.md\" target=\"_new\">https:\/\/github.com\/allenai\/allennlp\/blob\/main\/tutorials\/how_to\/elmo.md<\/a>)<\/li><\/ol>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/477061","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/477061\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/468299"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=477061"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}