\ub525\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc640 \ubcf5\uc7a1\ud55c \ud328\ud134 \uc778\uc2dd\uc758 \ud798\uc788\ub294 \ub3c4\uad6c\ub85c \uc790\ub9ac\uc7a1\uc558\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP), \ucd94\ucc9c \uc2dc\uc2a4\ud15c, \uc774\ubbf8\uc9c0 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uadf8 \uc751\uc6a9\uc774 \ub298\uc5b4\ub098\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud76c\uc18c \ud45c\ud604 \uae30\ubc18 \uc784\ubca0\ub529(sparse representation-based embedding)\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. \ud76c\uc18c \ud45c\ud604\uc740 \uace0\ucc28\uc6d0 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ud45c\ud604\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\uac8c \ub3c4\uc640\uc8fc\uba70, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ub370 \ud070 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/p>\n
\ud76c\uc18c \ud45c\ud604\uc774\ub780 \uc5b4\ub5a4 \ubb3c\ub9ac\uc801 \ub300\uc0c1\uc774\ub098 \ud604\uc0c1\uc744 \uace0\ucc28\uc6d0 \uacf5\uac04\uc5d0\uc11c \ud45c\ud604\ud560 \ub54c, \ub300\ubd80\ubd84\uc758 \uc694\uc18c\uac00 0\uc778 \ubca1\ud130\ub97c \uc774\uc6a9\ud574 \ud45c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ub9d0\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc774\ub7ec\ud55c \ud45c\ud604\uc740 \ub370\uc774\ud130\uac00 \uace0\ucc28\uc6d0\uc77c\uc218\ub85d \ub354\uc6b1 \ud6a8\uc728\uc801\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \ub2e8\uc5b4\ub97c \ud45c\ud604\ud560 \ub54c, BoW(Bag of Words) \ud45c\ud604\uc744 \uc0ac\uc6a9\ud558\uba74 \uac01 \ub2e8\uc5b4\uac00 \uace0\uc720\ud55c \uc778\ub371\uc2a4\ub97c \uac00\uc9c0\uba70 \ud574\ub2f9 \uc778\ub371\uc2a4\uc758 \uac12 \ub9cc\uc73c\ub85c \ub2e8\uc5b4\ub97c \ub098\ud0c0\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub807\uac8c \ub418\uba74 \uc0c1\ub2f9\uc218\uc758 \uc778\ub371\uc2a4 \uac12\uc740 0\uc774 \ub418\ubbc0\ub85c, \uc804\uccb4 \ub370\uc774\ud130\uc5d0\uc11c \ud6a8\uc728\uc801\uc73c\ub85c \uc800\uc7a5\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n
\uc608\ub97c \ub4e4\uc5b4, ‘\uc0ac\uacfc’, ‘\ubc14\ub098\ub098’, ‘\uccb4\ub9ac’\ub77c\ub294 \ub2e8\uc5b4\ub97c \uac01\uac01 0, 1, 2\ub85c \uc778\ub371\uc2f1\ud55c\ub2e4\uace0 \ud560 \ub54c, ‘\uc0ac\uacfc’\uc640 ‘\uccb4\ub9ac’\uac00 \ub4f1\uc7a5\ud558\ub294 \ubb38\uc7a5\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\ud604\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
[1, 0, 1]<\/code><\/pre>\n\uc704\uc758 \ubca1\ud130\uc5d0\uc11c 1\uc740 \ud574\ub2f9 \ub2e8\uc5b4\uc758 \uc874\uc7ac\ub97c \uc758\ubbf8\ud558\uace0 0\uc740 \ubd80\uc7ac\ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc774\ucc98\ub7fc \ud76c\uc18c \ud45c\ud604\uc740 \uacf5\uac04\uc801 \ud6a8\uc728\uc131\uacfc \uacc4\uc0b0\uc801 \ud6a8\uc728\uc131\uc744 \ub3d9\uc2dc\uc5d0 \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2. \uc784\ubca0\ub529(Embedding) \uac1c\uc694<\/h2>\n
\uc784\ubca0\ub529\uc774\ub77c\ub294 \uc6a9\uc5b4\ub294 \uace0\ucc28\uc6d0 \uacf5\uac04\uc5d0\uc11c\uc758 \uc0c1\uc9d5\uc801\uc778 \ub370\uc774\ud130\ub97c \uc800\ucc28\uc6d0 \uacf5\uac04\uc73c\ub85c \ubcc0\ud658\ud574\uc11c \ub354 \uc758\ubbf8 \uc788\ub294 \ud45c\ud604\uc73c\ub85c \ub9cc\ub4dc\ub294 \uacfc\uc815\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \ud2b9\ud788 \uace0\ucc28\uc6d0\uc758 \ubc94\uc8fc\ud615 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \ub54c \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
2.1 \uc784\ubca0\ub529\uc758 \uc911\uc694\uc131<\/h3>\n
\uc784\ubca0\ub529\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc5ec\ub7ec \uac00\uc9c0 \uc7a5\uc810\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc758 \ucc28\uc6d0 \uac10\uc18c\ub85c \ud559\uc2b5 \uc18d\ub3c4\uac00 \ube68\ub77c\uc9d0<\/li>\n
- \ube44\uc2b7\ud55c \ud56d\ubaa9\ub4e4 \uac04\uc758 \uad00\uacc4\ub97c \ub354 \uc798 \ud45c\ud604<\/li>\n
- \ubd88\ud544\uc694\ud55c \ub178\uc774\uc988\ub97c \uc904\uc784<\/li>\n<\/ul>\n
3. \ud76c\uc18c \ud45c\ud604 \uae30\ubc18 \uc784\ubca0\ub529<\/h2>\n
\ud76c\uc18c \ud45c\ud604\uc744 \uc0ac\uc6a9\ud560 \uacbd\uc6b0, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uc5d0\uc11c \uc911\uc694\ud55c \uc758\ubbf8\ub97c \ucd94\ucd9c\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4. \ub2e4\uc74c \uc139\uc158\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130 \uad6c\uc131<\/h3>\n
\ud76c\uc18c \ud45c\ud604 \uae30\ubc18\uc758 \uc784\ubca0\ub529\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574 \uc6b0\uc120 \ub370\uc774\ud130\ub97c \uad6c\uc131\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 \uc608\uc81c\uc5d0\uc11c\ub294 \ucf54\ub4dc\ub97c \ud1b5\ud574 \uc774\ub97c \uc27d\uac8c \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\n# \uc608\uc81c \ub370\uc774\ud130: \ub2e8\uc5b4 \ubaa9\ub85d\uacfc \uac01 \ub2e8\uc5b4\uc758 \uace0\uc720 \uc778\ub371\uc2a4\nword_list = ['\uc0ac\uacfc', '\ubc14\ub098\ub098', '\uccb4\ub9ac', '\ud3ec\ub3c4']\nword_to_index = {word: i for i, word in enumerate(word_list)}\n \n# \ubb38\uc7a5 \ub370\uc774\ud130 (\uc0ac\uacfc, \uccb4\ub9ac \ub4f1\uc7a5)\nsentences = [['\uc0ac\uacfc', '\uccb4\ub9ac'], ['\ubc14\ub098\ub098'], ['\ud3ec\ub3c4', '\uc0ac\uacfc', '\ubc14\ub098\ub098']]\n \n# \ud76c\uc18c \ud45c\ud604 \ubca1\ud130\ub85c \ubcc0\ud658\ud558\ub294 \ud568\uc218\ndef sentence_to_sparse_vector(sentence, word_to_index, vocab_size):\n vector = np.zeros(vocab_size)\n for word in sentence:\n if word in word_to_index:\n vector[word_to_index[word]] = 1\n return vector\n<\/code><\/pre>\n3.2 \ub370\uc774\ud130\uc14b \uad6c\uc131<\/h3>\n
\uc774\uc81c \uc704\uc5d0\uc11c \uc815\uc758\ud55c \ub370\uc774\ud130\ub97c \ud3ec\uc7a5\ud560 \ub370\uc774\ud130\uc14b \ud074\ub798\uc2a4\ub97c \uc815\uc758\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
class SparseDataset(Dataset):\n def __init__(self, sentences, word_to_index):\n self.sentences = sentences\n self.word_to_index = word_to_index\n self.vocab_size = len(word_to_index)\n\n def __len__(self):\n return len(self.sentences)\n\n def __getitem__(self, idx):\n sentence = self.sentences[idx]\n sparse_vector = sentence_to_sparse_vector(sentence, self.word_to_index, self.vocab_size)\n return torch.FloatTensor(sparse_vector)\n\n# \ub370\uc774\ud130\uc14b \ucd08\uae30\ud654\nsparse_dataset = SparseDataset(sentences, word_to_index)\ndataloader = DataLoader(sparse_dataset, batch_size=2, shuffle=True)\n<\/code><\/pre>\n4. \uc784\ubca0\ub529 \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\uc774\uc81c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc784\ubca0\ub529 \uacc4\uce35\uc744 \ud3ec\ud568\ud55c \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch.nn as nn\nimport torch.optim as optim\n\n# \uc784\ubca0\ub529 \ubaa8\ub378 \uc815\uc758\nclass EmbeddingModel(nn.Module):\n def __init__(self, vocab_size, embedding_dim):\n super(EmbeddingModel, self).__init__()\n self.embedding = nn.EmbeddingBag(vocab_size, embedding_dim, sparse=True)\n self.fc = nn.Linear(embedding_dim, 1)\n\n def forward(self, x):\n embedded = self.embedding(x)\n return self.fc(embedded)\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nvocab_size = len(word_to_index)\nembedding_dim = 2 # \uc784\ubca0\ub529 \ucc28\uc6d0 \uc124\uc815\nmodel = EmbeddingModel(vocab_size, embedding_dim)\n<\/code><\/pre>\n5. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub824\uba74 \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub294 \uc774 \uacfc\uc815\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n
def train(model, dataloader, epochs=10, lr=0.01):\n criterion = nn.BCEWithLogitsLoss() # \uc774\uc9c4 \ubd84\ub958 \uc190\uc2e4 \ud568\uc218\n optimizer = optim.SGD(model.parameters(), lr=lr)\n\n for epoch in range(epochs):\n for batch in dataloader:\n optimizer.zero_grad()\n output = model(batch)\n loss = criterion(output, torch.ones_like(output)) # \uc5ec\uae30\uc11c\ub294 \uc608\uc2dc\ub85c \ubaa8\ub450 1\ub85c \uc124\uc815\n loss.backward()\n optimizer.step()\n \n if (epoch + 1) % 5 == 0:\n print(f'Epoch [{epoch + 1}\/{epochs}], Loss: {loss.item():.4f}')\n\n# \ubaa8\ub378 \ud559\uc2b5 \uc2e4\ud589\ntrain(model, dataloader)\n<\/code><\/pre>\n6. \uacb0\uacfc \ubd84\uc11d<\/h2>\n
\ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \uc774\ud6c4\uc5d0\ub294 \uc784\ubca0\ub529 \uacb0\uacfc\ub97c \ubd84\uc11d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc784\ubca0\ub529\ub41c \ubca1\ud130\ub294 \uc904\uc5b4\ub4e0 \ucc28\uc6d0\uc5d0\uc11c \ub2e8\uc5b4\ub4e4 \uac04\uc758 \uc720\uc0ac\uc131\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \uc774 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\n\n# \ud559\uc2b5\ub41c \uc784\ubca0\ub529 \uac00\uc911\uce58 \uac00\uc838\uc624\uae30\nembeddings = model.embedding.weight.data.numpy()\n\n# PCA\ub97c \ud1b5\ud55c \ucc28\uc6d0 \ucd95\uc18c\npca = PCA(n_components=2)\nreduced_embeddings = pca.fit_transform(embeddings)\n\n# \uc2dc\uac01\ud654\nplt.figure(figsize=(8, 6))\nplt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1])\n\nfor idx, word in enumerate(word_list):\n plt.annotate(word, (reduced_embeddings[idx, 0], reduced_embeddings[idx, 1]))\nplt.title(\"\ub2e8\uc5b4 \uc784\ubca0\ub529 \uc2dc\uac01\ud654\")\nplt.xlabel(\"PCA Component 1\")\nplt.ylabel(\"PCA Component 2\")\nplt.grid()\nplt.show()\n<\/code><\/pre>\n7. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud76c\uc18c \ud45c\ud604 \uae30\ubc18 \uc784\ubca0\ub529\uc758 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \ud76c\uc18c \ud45c\ud604\uc740 \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc758 \ucc98\ub9ac\uc5d0 \uc788\uc5b4 \ub9e4\uc6b0 \ud6a8\uc728\uc801\uc774\uba70, \uc784\ubca0\ub529\uc744 \ud1b5\ud574 \ub2e8\uc5b4 \uac04\uc758 \uc758\ubbf8\uc801 \uc720\uc0ac\uc131\uc744 \uc27d\uac8c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\ubc95\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ucd94\uac00\uc801\uc73c\ub85c \uc784\ubca0\ub529 \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc774\ub098 \ub2e4\uc591\ud55c \uc544\ud0a4\ud14d\ucc98\ub97c \uc2e4\ud5d8\ud574\ubcf4\ub294 \uac83\ub3c4 \ub9e4\uc6b0 \ud765\ubbf8\ub85c\uc6b4 \uc791\uc5c5\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4. \ud76c\uc18c \ud45c\ud604 \uae30\ubc18 \uc784\ubca0\ub529\uc5d0 \ub300\ud55c \uacc4\uc18d\uc801\uc778 \uc5f0\uad6c\uc640 \uc2e4\uc2b5\uc744 \ud1b5\ud574, \ub354 \ub098\uc740 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \uc131\ub2a5\uc744 \ub192\uc5ec\ubcf4\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"