Joint Embedding of Words and Labels for Text Classification (1805.04174v1)

Abstract: Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors. We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels. The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones. Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information, in addition to input text sequences. Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.

• The paper presents the Label-Embedding Attentive Model (LEAM) that embeds words and labels in a shared space to boost classification accuracy.
• The paper employs a label-based attention mechanism that highlights key textual features, offering enhanced interpretability and efficiency.
• The paper demonstrates superior performance on large-scale datasets and real-world tasks like clinical text coding.

Overview of "Joint Embedding of Words and Labels for Text Classification"

The paper "Joint Embedding of Words and Labels for Text Classification" presents a novel approach to enhancing text classification accuracy through the joint embedding of words and labels. The authors propose a framework where both words and labels are represented in the same continuous space, thus enabling the model to learn label-based attentiveness in text sequences, thereby improving classification performance.

Summary of Contributions

The primary contribution of the paper is the introduction of the Label-Embedding Attentive Model (LEAM). This approach is built on the premise that labels serve as integral components in the semantic representation of text, and integrating them with word embeddings can enhance classification tasks. The key elements of the LEAM framework are as follows:

• Joint Embeddings: Both words and labels are embedded within the same latent space, allowing the model to directly utilize label information during the embedding phase. This contrasts with traditional approaches that utilize labels solely in the final classification phase.
• Label-Based Attention Mechanism: The model employs an attention mechanism that generates a text representation by considering the compatibility between embedded words and labels. This attention is guided by the semantic proximity of the textual features and the label embeddings, accentuating words pertinent to the label in question.
• Flexibility and Efficiency: The proposed method is computationally efficient, requiring fewer parameters compared to conventional RNN or CNN-based models, while delivering competitive classification accuracy.
• Interpretability: By leveraging label embeddings, the model inherently highlights key features relevant to the prediction, offering insights into which components of the text are most influential in relation to the classification label.

Implementation and Results

The model demonstrates its efficacy through experiments conducted on large-scale text classification datasets, such as AGNews and Yahoo! Answers, showcasing superior performance in terms of accuracy and computational efficiency. The LEAM model outperforms several prominent baselines, including complex neural architectures, thus confirming the efficacy of incorporating label embedding into text classification.

The paper also extends LEAM to a practical problem of medical code prediction on clinical text, illustrating its applicability to real-world tasks. This translates into direct utility in environments where interpretability and efficiency are paramount, such as in healthcare settings where vast amounts of textual patient data require precise and actionable labeling.

Theoretical Implications

From a theoretical perspective, the integration of label embeddings into the representation learning pipeline aligns with recent movements towards more cohesive and contextually aware NLP models. By embedding labels in the same space as words, LEAM proposes a unified approach that better captures the intrinsic relation of class labels with narrative text. This unified representation can propel advancements in multi-task learning, where shared representations across different yet related tasks could be beneficial.

Future Directions

In terms of future advancements, further exploration into optimizing label embeddings for datasets that lack straightforward label descriptions could augment the adaptability of LEAM across various domains. Moreover, since the model's performance hinges on the robustness of the embedding space, improved methods for initializing and refining these embeddings could yield substantial gains.

The LEAM framework poses interesting questions for further research into the impact of label embedding across diverse NLP applications, as well as the exploration of hybrid embedding strategies that integrate dynamically learned labels with pre-existing semantic knowledge bases.

In conclusion, the introduction of joint embedding of words and labels marks a significant step in refining text classification approaches, with LEAM offering a coherent, efficient, and interpretable model that opens up new avenues for future research and application.