Information Extraction: Relations, Events, and Knowledge

Module: Natural Language Processing | Difficulty: Advanced

Relation Extraction

Event Extraction

OpenIE Triples

Knowledge Graph Embeddings

| Model | Score Function | Complexity | |-------|---------------|------------| | TransE | | | | RotatE | | | | ComplEx | | |

import torch
import torch.nn as nn

class TransE(nn.Module):
    def __init__(self, n_entities, n_relations, dim=100):
        super().__init__()
        self.entity_emb = nn.Embedding(n_entities, dim)
        self.relation_emb = nn.Embedding(n_relations, dim)
    def score(self, head, relation, tail):
        h = self.entity_emb(head)
        r = self.relation_emb(relation)
        t = self.entity_emb(tail)
        return -torch.norm(h + r - t, p=2, dim=1)

Research Insight: The key challenge in information extraction is handling noisy text. Pre-trained language models improve IE performance by 15-25% because they understand context and can handle ambiguity better than pattern-based systems.