Natural Language Processing (almost) from Scratch

Abstract: We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

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Synopsis

Overview

  • Keywords: Natural Language Processing, Neural Networks, Unsupervised Learning, Multi-task Learning, Internal Representations
  • Objective: Propose a unified neural network architecture that minimizes reliance on task-specific engineering for various NLP tasks.
  • Hypothesis: A single learning system can discover adequate internal representations from large unlabeled datasets, leading to good performance across multiple NLP tasks without extensive feature engineering.

Background

  • Preliminary Theories:

    • Neural Networks: A class of models capable of learning complex patterns through layers of interconnected nodes, which can be trained using backpropagation.
    • Transfer Learning: The practice of transferring knowledge gained from one task to improve performance on another, particularly useful in scenarios with limited labeled data.
    • Unsupervised Learning: A type of machine learning that finds patterns in data without labeled responses, often used to pre-train models on large datasets.
    • Multi-task Learning: An approach where a model is trained on multiple tasks simultaneously, allowing it to leverage shared information and improve generalization.
  • Prior Research:

    • 2003: CoNLL shared tasks established benchmarks for various NLP tasks, including Named Entity Recognition (NER) and Semantic Role Labeling (SRL).
    • 2005: Development of semi-supervised learning methods, which combine labeled and unlabeled data to improve model performance.
    • 2007: Introduction of word embeddings, which capture semantic relationships between words in a continuous vector space, enhancing performance in NLP tasks.

Methodology

  • Key Ideas:

    • Unified Model: A single neural network architecture that can be applied to multiple NLP tasks, reducing the need for task-specific feature engineering.
    • Internal Representations: The model learns to create its own representations from raw text data, leveraging large unlabeled datasets for training.
    • Multi-task Training: The architecture is designed to share parameters across tasks, allowing for joint learning and improved performance through shared representations.
  • Experiments:

    • Benchmark Tasks: Evaluated on Part-Of-Speech tagging (POS), chunking, NER, and SRL using standard datasets.
    • Language Modeling: Utilized large unlabeled datasets (approximately 852 million words) to train a language model, which informed the internal representations used in supervised tasks.
    • Performance Metrics: Employed accuracy for POS and F1 scores for chunking, NER, and SRL to assess model performance.
  • Implications: The design emphasizes the importance of learning from raw data, suggesting that effective NLP systems can be built with minimal reliance on handcrafted features.

Findings

  • Outcomes:

    • Achieved competitive performance across all benchmark tasks, demonstrating that a unified approach can yield results comparable to state-of-the-art systems.
    • The model's internal representations, learned from unlabeled data, significantly improved performance when transferred to supervised tasks.
    • Multi-task training resulted in better generalization, as shared representations reduced overfitting on individual tasks.
  • Significance: This research challenges the traditional reliance on task-specific features in NLP, advocating for a more generalized approach that can adapt to various tasks without extensive engineering.

  • Future Work: Exploration of more complex architectures, integration of additional linguistic features, and further refinement of the model's ability to leverage unlabeled data.

  • Potential Impact: If pursued, these avenues could lead to more robust NLP systems capable of understanding and processing language with minimal human intervention, advancing the field towards more generalized artificial intelligence applications.

Notes

Meta

Published: 2011-03-02

Updated: 2025-08-27

URL: https://arxiv.org/abs/1103.0398v1

Authors: Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, Pavel Kuksa

Citations: 7486

H Index: 318

Categories: cs.LG, cs.CL

Model: gpt-4o-mini