Detecting Personal Life Events from Twitter by Multi-Task LSTM

Yen, An-Zi; Huang, Hen-Hsen; Chen, Hsin-Hsi

doi:10.1145/3184558.3186909

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…Certain efforts [12]- [14] characterize users based on their online communication and web-page visiting activities. Detecting life events [15], [16] from tweets has also been addressed.…”

Section: Related Workmentioning

confidence: 99%

“…In order to incorporate external information, Ghosh et al [26] build a contextual LSTM model that adds the contextual feature into the calculation of each gate function. Yen et al [15] utilize a multi-task LSTM and include contextual information by simply concatenating the features. Finally, hierarchical LSTM models are built [7], [27] that stack LSTM models with different levels of sequential data.…”

Section: Related Workmentioning

confidence: 99%

“…1. LSTM for Text Classification2) Joint-LSTM: Similar to the idea of Yen et al[15], we design Joint-LSTM (J-LSTM) model to concatenate the flat representations of the sequential input of content and contextual features before feeding it to the output layer. Figure2shows an example design of Joint-LSTM model.…”

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confidence: 99%

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Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model

Cui¹,

Agrawal²,

Ramnath³

2019

Preprint

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This paper presents techniques to detect the offline activity a person is engaged in when she is tweeting (such as dining, shopping or entertainment), in order to create a dynamic profile of the user, for uses such as better targeting of advertisements. To this end, we propose a hybrid LSTM model for rich contextual learning, along with studies on the effects of applying and combining multiple LSTM based methods with different contextual features. The hybrid model is shown to outperform a set of baselines and state-of-the-art methods. Finally, this paper presents an orthogonal validation with a realcase application. Our model generates an offline activity analysis for the followers of several well-known accounts, which is quite representative of the expected characteristics of these accounts.

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“…Certain efforts [12]- [14] characterize users based on their online communication and web-page visiting activities. Detecting life events [15], [16] from tweets has also been addressed.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model

Cui¹,

Agrawal²,

Ramnath³

2019

Preprint

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“…Previous research on life event extraction mainly focuses on life events from microblogs or social media platforms such as Twitter (Li et al, 2014;Yen et al, 2018Yen et al, , 2019. However, these events from a given fixed passage are static.…”

Section: Introductionmentioning

confidence: 99%

A study of adults' orientations to learning at the national open university of Taiwan

Chen¹

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Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance. The issues of collecting and extracting personal life events have emerged. People often share their life experiences with others through conversations. However, extracting life events from conversations is rarely explored. In this paper, we present Life Event Dialog, a dataset containing fine-grained life event annotations on conversational data. In addition, we initiate a novel conversational life event extraction task and differentiate the task from the public event extraction or the life event extraction from other sources like microblogs. We explore three information extraction (IE) frameworks to address the conversational life event extraction task: OpenIE, relation extraction, and event extraction. A comprehensive empirical analysis of the three baselines is established. The results suggest that the current event extraction model still struggles with extracting life events from human daily conversations. Our proposed life event dialog dataset and in-depth analysis of IE frameworks will facilitate future research on life event extraction from conversations.

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“…Description strings are formed by the FIs, presumably, by formatting a template with information regarding a specific transaction. At first glance it might seem that Recurrent Neural Networks (RNNs) and other machine learning approaches are not the right tool for inferring these deterministic mappings (although commonly used successfully for tasks over short strings [4,5,8]). The simplicity and accessibility of these methods, and excellent results obtained in our task (see Section 4) lead us to favor them over more traditional data mining tools designed specifically for finding patterns in strings.…”

Section: Introductionmentioning

confidence: 99%

Identifying Financial Institutions by Transaction Signatures

Haas,

Horesh,

Shahar

et al. 2018

Preprint

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Financial data aggregators and Personal Financial Management (PFM) services are software products that help individuals manage personal finances by collecting information from multiple accounts at various Financial Institutes (FIs), presenting data in a coherent and concentrated way, and highlighting insights and suggestions. Money transfers consist of two sides and a direction. From the perspective of a financial data aggregator, an incoming transaction consists of a date, an amount, and a description string, but not the explicit identity of the sending FI. In this paper we investigate supervised learning based methods to infer the identity of the sending FI from the description string of a money transfer transaction, using a blend of traditional and RNN based NLP methods. Our approach is based on the observation that the textual description field associated with a transactions is subjected to various types of normalizations and standardizations, resulting in unique patterns that identify the issuer. We compare multiple methods using a large real-word dataset of over 10 million transactions.

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Detecting Personal Life Events from Twitter by Multi-Task LSTM

Cited by 11 publications

References 5 publications

Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model

Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model

A study of adults' orientations to learning at the national open university of Taiwan

Identifying Financial Institutions by Transaction Signatures

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