Updated TED method
This commit is contained in:
Henry Dowd
@@ -656,6 +656,223 @@
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"results, methods = evaluate_baseline_methods(test_pairs)\n",
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"print_comparison_table(results, test_pairs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "06db7e83",
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"metadata": {},
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"source": [
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"### Tree edit Distance analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c62457eb",
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"metadata": {},
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"outputs": [],
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"source": [
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"import spacy\n",
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"\n",
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"# Load spaCy model for dependency parsing\n",
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"nlp = spacy.load(\"en_core_web_lg\")\n",
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"print(\"Loaded spaCy model for Tree Edit Distance\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c5905e3b",
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"metadata": {},
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"source": [
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"##### Build trees"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2aea0b77",
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"metadata": {},
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"outputs": [],
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"source": [
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"def build_tree_from_dependencies(text):\n",
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" \"\"\"Build hierarchical tree structure from dependency parse.\"\"\"\n",
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" doc = nlp(text)\n",
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" \n",
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" # Create node dictionary with parent-child relationships\n",
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" tree = {}\n",
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" root_token = None\n",
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" \n",
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" for token in doc:\n",
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" tree[token.i] = {\n",
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" 'text': token.text,\n",
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" 'lemma': token.lemma_,\n",
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" 'pos': token.pos_,\n",
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" 'dep': token.dep_,\n",
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" 'head_id': token.head.i,\n",
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" 'children': []\n",
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" }\n",
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" \n",
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" # Build parent-child relationships\n",
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" for token in doc:\n",
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" if token.head.i != token.i: # Not root\n",
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" tree[token.head.i]['children'].append(token.i)\n",
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" else: # Root node\n",
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" root_token = token.i\n",
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" \n",
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" return tree, root_token\n",
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"\n",
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"print(\"Tree building function loaded\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b45d5916",
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"metadata": {},
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"source": [
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"##### Helper Functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a5e5fa5a",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_nodes_postorder(node_id, tree):\n",
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" \"\"\"Get all nodes in postorder traversal (children before parents).\"\"\"\n",
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" nodes = []\n",
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" if node_id in tree:\n",
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" for child_id in tree[node_id]['children']:\n",
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" nodes.extend(get_nodes_postorder(child_id, tree))\n",
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" nodes.append(node_id)\n",
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" return nodes\n",
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"\n",
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"def node_label(node_id, tree):\n",
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" \"\"\"Get label for a node (POS tag + dependency relation).\"\"\"\n",
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" if node_id in tree:\n",
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" return (tree[node_id]['pos'], tree[node_id]['dep'])\n",
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" return ('', '')\n",
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"\n",
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"def nodes_match(node_id1, tree1, node_id2, tree2):\n",
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" \"\"\"Check if two nodes have matching labels.\"\"\"\n",
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" label1 = node_label(node_id1, tree1)\n",
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" label2 = node_label(node_id2, tree2)\n",
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" return label1 == label2\n",
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"\n",
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"print(\"Helper functions loaded\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5176cf24",
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"metadata": {},
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"source": [
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"##### Tree edit distance function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3ab8c40c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def tree_edit_distance_zhang_shasha(tree1, tree2, root1, root2):\n",
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" \"\"\"\n",
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" Full Tree Edit Distance using Zhang-Shasha algorithm.\n",
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" Preserves hierarchical structure and node ordering.\n",
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" Returns: Normalized similarity score (0-1)\n",
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" \"\"\"\n",
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" \n",
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" # get postorder traversals\n",
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" nodes1 = get_nodes_postorder(root1, tree1)\n",
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" nodes2 = get_nodes_postorder(root2, tree2)\n",
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" \n",
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" m, n = len(nodes1), len(nodes2)\n",
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" \n",
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" # DP table\n",
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" dp = [[0] * (n + 1) for _ in range(m + 1)]\n",
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" \n",
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" # Base cases\n",
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" for i in range(m + 1):\n",
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" dp[i][0] = i\n",
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" for j in range(n + 1):\n",
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" dp[0][j] = j\n",
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" \n",
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" # Fill DP table\n",
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" for i in range(1, m + 1):\n",
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" for j in range(1, n + 1):\n",
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" node1 = nodes1[i - 1]\n",
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" node2 = nodes2[j - 1]\n",
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" \n",
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" if nodes_match(node1, tree1, node2, tree2):\n",
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" cost = dp[i - 1][j - 1]\n",
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" else:\n",
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" delete_cost = dp[i - 1][j] + 1\n",
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" insert_cost = dp[i][j - 1] + 1\n",
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" replace_cost = dp[i - 1][j - 1] + 1\n",
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" cost = min(delete_cost, insert_cost, replace_cost)\n",
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" \n",
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" dp[i][j] = cost\n",
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" \n",
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" # convert to similarity\n",
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" max_size = max(m, n)\n",
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" if max_size == 0:\n",
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" return 1.0\n",
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" \n",
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" edit_distance = dp[m][n]\n",
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" similarity = 1.0 - (edit_distance / max_size)\n",
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" \n",
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" return max(0.0, similarity)\n",
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"\n",
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"def tree_edit_distance_similarity(sent1, sent2):\n",
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" \"\"\"Wrapper function for Tree Edit Distance similarity.\"\"\"\n",
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" if not sent1.strip() or not sent2.strip():\n",
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" return 0.0\n",
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" \n",
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" try:\n",
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" tree1, root1 = build_tree_from_dependencies(sent1)\n",
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" tree2, root2 = build_tree_from_dependencies(sent2)\n",
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" return tree_edit_distance_zhang_shasha(tree1, tree2, root1, root2)\n",
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" except:\n",
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" return 0.0\n",
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"\n",
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"print(\"Tree Edit Distance algorithm loaded\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3bfc1dcd",
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"metadata": {},
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"source": [
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"##### Standalone running TED"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "80f72ef3",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Tree Edit Distance - Standalone Tests\")\n",
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"print(\"=\" * 100)\n",
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"\n",
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"test_cases = [\n",
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" (\"The cat sat on the mat.\", \"The cat sat on the mat.\"), # Exact copy\n",
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" (\"The cat sat on the mat.\", \"On the mat, the cat was sitting.\"), # Structural change\n",
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" (\"The cat sat on the mat.\", \"The feline rested on the rug.\"), # Synonym replacement\n",
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" (\"The cat sat on the mat.\", \"The dog ran in the park.\"), # Different content\n",
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"]\n",
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"\n",
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"for sent1, sent2 in test_cases:\n",
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" sim = tree_edit_distance_similarity(sent1, sent2)\n",
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" print(f\"\\n{sent1:<45} vs\")\n",
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" print(f\"{sent2:<45}\")\n",
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" print(f\"TED Similarity: {sim:.3f}\")\n",
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" print(\"-\" * 100)"
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]
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}
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],
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"metadata": {
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@@ -674,7 +891,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.12"
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"version": "3.14.2"
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}
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},
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"nbformat": 4,
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