Tweet2Vec: Character-Based Distributed Representations for Social Media (1605.03481v2)

Abstract: Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vector-space representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significantly better when the input contains many out-of-vocabulary words or unusual character sequences. Our tweet2vec encoder is publicly available.

• The paper presents a character-level encoder that predicts hashtags and outperforms traditional word-based models.
• It employs a Bi-GRU that captures both local and non-local character dependencies, handling noisy data and out-of-vocabulary words effectively.
• Empirical results demonstrate improved precision@1 scores, with tweet2vec achieving up to 32.9% on rare-word datasets, underscoring its practical value.

Overview of Tweet2Vec: Character-Based Distributed Representations for Social Media

The paper introduces a novel approach to creating vector-space representations of social media posts, specifically tweets, through a character-level model named tweet2vec. The inherent difficulties posed by social media texts—such as informal language, misspellings, and the use of special characters—challenge traditional word-based NLP techniques. Addressing this, tweet2vec leverages the granularity of character-based models to handle these issues more effectively.

Methodology

Tweet2vec employs a Bi-directional Gated Recurrent Unit (Bi-GRU) encoder to process sequences of characters, including white-space and special symbols. The model is trained to predict user-annotated hashtags, which serve as a form of supervision. This approach allows the model to derive embeddings by capturing both local and non-local dependencies in the text, reflected in its ability to manage out-of-vocabulary (OOV) words and sequences effectively.

This character-based model is compared against a word-level baseline that relies on fixed vocabularies, often grouping infrequent and unseen word types under a generic UNKNOWN token. This approach is limited by the static nature of word vectors and the need for preprocessing like word-segmentation, which can be error-prone in the noisy domain of Twitter.

Experiments and Results

The authors present robust evidence of tweet2vec's superiority over word-based models in predicting hashtags. The character-level approach shows improved average precision@1 and recall@10, particularly in examples with many OOV words, which are a common feature of social media content. The experiments demonstrate that tweet2vec provides a more flexible system that scales better in the face of extensive vocabulary and varying input token sequences.

Performance metrics were evaluated across different test sets, including those with rare and frequent words. For instance, the tweet2vec model achieved a precision@1 score of 28.4%, outperforming the word model at 24.1% in the full test set scenario. Additionally, in a rare words test set, tweet2vec scored 32.9% precision@1 versus 20.4% from the word model, further emphasizing its capability in managing rare and unseen terms effectively.

Implications and Future Work

The implications of this research extend both practically and theoretically. Practically, the character-based methodology is advantageous in settings where language independence is crucial or where preprocessing challenges exist due to noisy data. The model's capacity to generalize to unseen data points suggests applications in sentiment analysis, trend detection, and possibly in multilingual contexts, given its reliance on character compositions rather than language-specific tokens.

Theoretically, tweet2vec supports the broader shift in NLP towards models that leverage sub-word information to bolster semantic understanding. Future research could explore enhancing the tweet2vec framework with a character-level decoder, thereby facilitating the generation of novel hashtags not seen in the training data. Investigations into deploying tweet2vec in real-world systems for tracking societal trends or public health data, such as monitoring disease spread, could demonstrate the utility of this character-based approach to NLP.

In conclusion, tweet2vec provides a substantial improvement upon current models, illustrating the potential for character-level embeddings to enhance social media analytics. This paper contributes to the field by addressing significant gaps in existing approaches, offering a publicly available encoder to spur further exploration and development in semantic processing of social media text.