Distributed Representations of Sentences and Documents
Abstract: Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.
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Synopsis
Overview
- Keywords: Distributed Representations, Sentences, Documents, Paragraph Vector, Machine Learning
- Objective: Propose an unsupervised algorithm for learning fixed-length feature representations from variable-length texts.
- Hypothesis: The Paragraph Vector model can effectively represent sentences and documents while overcoming the limitations of traditional bag-of-words models.
- Innovation: Introduction of the Paragraph Vector model that captures semantic meaning and word order without requiring parsing or extensive labeled data.
Background
Preliminary Theories:
- Bag-of-Words Model: A common text representation that ignores word order and semantics, leading to loss of contextual meaning.
- Word Embeddings: Techniques like Word2Vec that learn vector representations of words based on their context, capturing semantic similarities.
- Neural Language Models: Frameworks that use neural networks to predict words based on surrounding context, enhancing understanding of language structure.
- Distributed Memory Models: Models that maintain a memory of context (like paragraph vectors) to improve prediction tasks in language processing.
Prior Research:
- 2006: Introduction of neural probabilistic language models that laid the groundwork for word embeddings.
- 2011: Development of recursive neural networks for semantic compositionality, which required parsing and labeled data.
- 2013: Advances in word vector representations that demonstrated the effectiveness of unsupervised learning in capturing semantics.
Methodology
Key Ideas:
- Paragraph Vector (PV): Represents documents as dense vectors trained to predict words in the context of the paragraph.
- Distributed Memory (PV-DM): Combines paragraph vectors with word vectors to predict the next word, leveraging context.
- Distributed Bag of Words (PV-DBOW): A simpler version that predicts words from randomly sampled contexts, reducing data storage needs.
Experiments:
- Datasets: Utilized the IMDB dataset for sentiment analysis and Stanford Sentiment Treebank for text classification.
- Metrics: Evaluated performance based on error rates in sentiment classification tasks, comparing against traditional models like bag-of-words and recursive neural networks.
Implications: The methodology allows for efficient representation of variable-length texts without the need for extensive labeled datasets, making it applicable across various domains.
Findings
Outcomes:
- Performance: Paragraph Vector outperformed bag-of-words models by significant margins (up to 30% relative improvement in text classification tasks).
- Generalization: Demonstrated ability to generalize across different lengths of text, from sentences to entire documents.
- Semantic Capture: Successfully captured semantic relationships, with word vectors reflecting meaningful distances in the vector space.
Significance: The research challenges the dominance of bag-of-words models, providing a robust alternative that retains contextual and semantic information.
Future Work: Suggested exploration of Paragraph Vector applications in non-text domains and further refinement of the model to enhance efficiency and accuracy.
Potential Impact: If further developed, these methods could revolutionize how text data is processed in machine learning, leading to more nuanced understanding and applications in natural language processing.