| Challenge: | Hashtags are used to add metadata to textual utterances, but their semantic content is difficult to infer as they often contain multiple tokens joined together. |
| Approach: | They propose to use a dataset of 12,594 hashtags to infer hashtag semantics . they propose to frame the problem as a pairwise ranking problem between candidate segmentations . |
| Outcome: | The proposed methods show 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. |
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| Challenge: | Hashtag segmentation is the task of breaking a hashtag into constituent tokens . hashtags are often written in unique ways, including spelling variations, and special characters. |
| Approach: | They propose a dataset that breaks hashtags into constituent tokens to train and validate models. |
| Outcome: | The proposed dataset provides an alternate set of hashtags to build and validate hashtag segmentation models. |
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (2021.emnlp-main)
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| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
| Approach: | They propose a personalized topic attention model that captures salient contents to personalize hashtag contexts by predicting how likely a user will post with a hashtag. |
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Multi-Task Learning for Sequence Tagging: An Empirical Study (C18-1)
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| Challenge: | Existing work on "pairwise" MTL has been validated in sequence tagging but key issues remain about its effectiveness. |
| Approach: | They propose three general multi-task learning approaches on 11 sequence tagging tasks. |
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Text Segmentation as a Supervised Learning Task (N18-2)
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| Challenge: | Existing datasets for text segmentation are small in size and do not represent the natural distribution of text in documents. |
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What can we learn from Semantic Tagging? (D18-1)
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| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
| Approach: | They employ semantic tagging as an auxiliary task for three NLP tasks . they compare full neural network sharing, partial neural network shared and learning what to share . |
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Twitter Trend Extraction: A Graph-based Approach for Tweet and Hashtag Ranking, Utilizing No-Hashtag Tweets (2020.lrec-1)
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Zahra Majdabadi, Behnam Sabeti, Preni Golazizian, Seyed Arad Ashrafi Asli, Omid Momenzadeh, Reza Fahmi
| Challenge: | Twitter has become a major platform for users to express their opinions on any topic and engage in debates. |
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Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (N19-1)
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| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
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Leveraging Hashtag Networks for Multimodal Popularity Prediction of Instagram Posts (2022.lrec-1)
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| Challenge: | Existing popularity prediction approaches reduce hashtags to simple features such as hashtag length or number of hashtags in a post. |
| Approach: | They propose a multimodal framework to predict popular influencer posts on Instagram using post captions, image, hashtag network and topic model. |
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Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces (N18-1)
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| Challenge: | Multi-task learning and semi-supervised learning are successful paradigms for learning in scenarios with limited labelled data. |
| Approach: | They propose to induce a joint embedding space between disparate label spaces and learning transfer functions between label embeddments to leverage unlabelled data and auxiliary, annotated datasets. |
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Addressing Segmentation Ambiguity in Neural Linguistic Steganography (2022.aacl-short)
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| Challenge: | Recent studies on neural linguistic steganography ignore the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper’s suspicion. |
| Approach: | They propose to decode a secret message in a way that does not arouse suspicion of the eavesdropper. |
| Outcome: | The proposed techniques are applicable to languages without explicit word boundaries. |