Linrui Zhang


2020

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Affect inTweets: A Transfer Learning Approach
Linrui Zhang | Hsin-Lun Huang | Yang Yu | Dan Moldovan
Proceedings of the 12th Language Resources and Evaluation Conference

People convey sentiments and emotions through language. To understand these affectual states is an essential step towards understanding natural language. In this paper, we propose a transfer-learning based approach to inferring the affectual state of a person from their tweets. As opposed to the traditional machine learning models which require considerable effort in designing task specific features, our model can be well adapted to the proposed tasks with a very limited amount of fine-tuning, which significantly reduces the manual effort in feature engineering. We aim to show that by leveraging the pre-learned knowledge, transfer learning models can achieve competitive results in the affectual content analysis of tweets, compared to the traditional models. As shown by the experiments on SemEval-2018 Task 1: Affect in Tweets, our model ranking 2nd, 4th and 6th place in four of its subtasks proves the effectiveness of our idea.

2018

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Chinese Relation Classification using Long Short Term Memory Networks
Linrui Zhang | Dan Moldovan
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Rule-based vs. Neural Net Approaches to Semantic Textual Similarity
Linrui Zhang | Dan Moldovan
Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing

This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper, we present an end-to-end attention-based Bi-LSTM neural network system that solely takes word-level features, without expensive feature engineering work or the usage of external resources. By comparing its performance with traditional rule-based systems against SemEval-2012 benchmark, we make an assessment on the limitations and strengths of neural net systems to rule-based systems on Semantic Textual Similarity.