Representation learning for very short texts using weighted word embedding aggregation (1607.00570v1)

Abstract: Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic word embeddings and frequency information to arrive at low-dimensional representations for short texts designed to capture semantic similarity. For this purpose we designed a weight-based model and a learning procedure based on a novel median-based loss function. This paper discusses the details of our model and the optimization methods, together with the experimental results on both Wikipedia and Twitter data. We find that our method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining. Our method is therefore capable of retaining most of the semantic information in the text, and is applicable out-of-the-box.

Representation Learning for Very Short Texts Using Weighted Word Embedding Aggregation

The paper at hand discusses a novel approach for creating vector representations of very short texts, with a primary focus on improving semantic capture through weighted word embedding aggregation. Traditional models, like tf-idf, fall short in effectively representing such texts due to their sparse and noisy vocabulary usage. The researchers developed this method inspired by the need to enhance event detection, opinion mining, and news recommendation from short text data such as tweets.

The authors propose a method that utilizes semantic word embeddings along with frequency information to derive low-dimensional representations aimed at capturing semantic similarity. The innovation lies in a weight-based model underpinned by a novel median-based loss function. This approach is evaluated in context with data sourced from Wikipedia and Twitter, showcasing its superiority over existing baseline approaches such as mean and max pooling of embeddings, concatenation of these representations, or the usage of unaltered tf-idf vectors.

Key Methodological Insights

• Weighted Word Embeddings: The core methodology assigns weights to words based on their inverse document frequency (idf), allowing words critical to the semantic interpretation to contribute more significantly to the final text representation. This weight determination is central to achieving a more semantically relevant aggregation.
• Median-Based Loss Function: The paper introduces a loss function designed to minimize the impact of outliers by focusing on the median rather than the mean. This feature is significant for better performance in handling highly noisy data such as Twitter feeds.
• Adaptability: An important characteristic emphasized is the technique’s robustness and adaptability across different word embeddings without requiring retraining. This quality provides notable practical utility, particularly in diverse operational contexts.

Experimental Evaluation

The experiments compare the method to standard baselines like tf-idf and unweighted aggregation. The results from experiments conducted with varying text lengths (fixed and variable) on both Wikipedia and Twitter data sets confirm that the proposed method significantly outperforms the baselines. For instance, using Wikipedia embeddings, the proposed model achieved a split error of 14.06% on fixed-length texts, significantly lower than tf-idf and mean aggregation methods.

The median-based loss outperforms the contrastive loss function in settings with variable-length texts, indicating the former's superior capability to handle variability and noise inherent in datasets like Twitter.

Implications and Speculation on Future Work

The implications of this research are multifaceted. Practically, the approach presents a plug-and-play solution for advanced semantic analysis of short texts across various platforms. Theoretically, it challenges existing paradigms by effectively leveraging weighted combinations of embeddings for enhanced semantic similarity tasks.

Future developments in AI could build on this foundation by exploring more intricate weight assignment algorithms, possibly integrating additional context-aware features or hybrid models that combine structured and unstructured data inputs. Furthermore, expanding into more diverse datasets could present richer insights into the adaptability and limitations of the method across different language constructs and applications.

Overall, the paper offers substantial contributions to the field of natural language processing, particularly in the context of short text analysis, and sets the stage for continued exploration and refinement of representation learning methodologies.