Automatic Keyword Extraction on Twitter

Marujo, Luís; Wang, Ling; Trancoso, Isabel; Dyer, Chris; Black, Alan W.; Gershman, Anatole; Matos, David Martins de; Neto, João Paulo; Carbonell, Jaime G.

doi:10.3115/v1/p15-2105

Cited by 46 publications

(34 citation statements)

References 26 publications

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“…Because multiple tweets are usually organized by topic, many document-level approaches can also be adopted to achieve the task. In contrast with the previous methods, Marujo et al (2015) focused on the task of extracting keywords from single tweets. They used several unsupervised methods and word embeddings to construct features.…”

Section: Introductionmentioning

confidence: 99%

Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Zhang

Wang

Gong

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

172

133

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Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. The task of automatically extracting them have received considerable attention in recent decades. Different from previous studies, which are usually focused on automatically extracting keyphrases from documents or articles, in this study, we considered the problem of automatically extracting keyphrases from tweets. Because of the length limitations of Twitter-like sites, the performances of existing methods usually drop sharply. We proposed a novel deep recurrent neural network (RNN) model to combine keywords and context information to perform this problem. To evaluate the proposed method, we also constructed a large-scale dataset collected from Twitter. The experimental results showed that the proposed method performs significantly better than previous methods.

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Section: Introductionmentioning

confidence: 99%

Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Zhang

Wang

Gong

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

172

133

View full text Add to dashboard Cite

show abstract

“…However, our target is to extract keyphrases from single tweets. Along this line, the approaches proposed by Marujo et al [30] and Zhang et al [63] work for single tweets. Marujo et al [30] showed that word embeddings in a system such as MAUI [31] performs better than the TF-IDF baseline [53].…”

Section: Related Workmentioning

confidence: 99%

“…Along this line, the approaches proposed by Marujo et al [30] and Zhang et al [63] work for single tweets. Marujo et al [30] showed that word embeddings in a system such as MAUI [31] performs better than the TF-IDF baseline [53]. Furthermore, the Joint-Layer-RNN model proposed by Zhang et al [63] was shown to be even better than the model proposed by Marujo et al [30].…”

Section: Related Workmentioning

confidence: 99%

Keyphrase Extraction from Disaster-related Tweets

Ray

Caragea

2019

The World Wide Web Conference

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While keyphrase extraction has received considerable attention in recent years, relatively few studies exist on extracting keyphrases from social media platforms such as Twitter, and even fewer for extracting disaster-related keyphrases from such sources. During a disaster, keyphrases can be extremely useful for filtering relevant tweets that can enhance situational awareness. Previously, joint training of two different layers of a stacked Recurrent Neural Network for keyword discovery and keyphrase extraction had been shown to be effective in extracting keyphrases from general Twitter data. We improve the model's performance on both general Twitter data and disaster-related Twitter data by incorporating contextual word embeddings, POS-tags, phonetics, and phonological features. Moreover, we discuss the shortcomings of the often used F1-measure for evaluating the quality of predicted keyphrases with respect to the ground truth annotations. Instead of the F1-measure, we propose the use of embedding-based metrics to better capture the correctness of the predicted keyphrases. In addition, we also present a novel extension of an embedding-based metric. The extension allows one to better control the penalty for the difference in the number of ground-truth and predicted keyphrases.

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“…Recently, keyphrase extraction methods have been extended to social media texts (Bellaachia & Al-Dhelaan, 2012;Marujo et al, 2015;Zhang et al, 2016;Zhao et al, 2011); for example, Twitter and Sina Weibo. Marujo et al (2015) extracted a keyphrase from a single text. This model is based on the MAUI toolkit (2010), which trains a decision tree over a large set of manually engineered features; for example, TF-IDF score, Brown clustering information, and word vectors.…”

Section: Related Workmentioning

confidence: 99%

“…To date, most studies on related fields have focused on the usage of ranking-based models (Bellaachia & Al-Dhelaan, 2012;Marujo et al, 2015) or sequence-tagging models (Zhang, Wang, Gong, & Huang, 2016) via treating messages as independent documents. However, because of the severe data sparsity issue, it is arguable that these methods are suboptimal in recognizing key content from short and informal microblog messages.…”

Section: Introductionmentioning

confidence: 99%

Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction

Zhang

2019

Asso for Info Science & Tech

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Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word‐level indicative representation and message‐level indicative representation hierarchically. In both of the modules, we apply character‐level representations, which enables the model to explore morphological features and deal with the out‐of‐vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real‐life data sets indicate that our model outperforms state‐of‐the‐art models from previous studies.

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Automatic Keyword Extraction on Twitter

Cited by 46 publications

References 26 publications

Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Keyphrase Extraction from Disaster-related Tweets

Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction

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