# An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction

arXiv: https://arxiv.org/abs/2109.01238 · HTML: https://mlsanity.com/paper/2109.01238/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks](https://mlsanity.com/paper/1810.09536/) (2018)
- [Neural Machine Translating from Natural Language to SPARQL](https://mlsanity.com/paper/1906.09302/) (2019)
- [Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2001.01989/) (2020)
- [Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning](https://mlsanity.com/paper/2010.13378/) (2020)
- [A Critical Analysis of Biased Parsers in Unsupervised Parsing](https://mlsanity.com/paper/1909.09428/) (2019)

## Built on by (most cited first, 5 of 5)

- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Improving Graph-Based Text Representations with Character and Word Level N-grams](https://mlsanity.com/paper/2210.05999/) (2022)
- [Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2402.15370/) (2024)
- [Training Entire-Space Models for Target-oriented Opinion Words Extraction](https://mlsanity.com/paper/2204.07337/) (2022)
- [Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement](https://mlsanity.com/paper/2305.11034/) (2023)

## Abstract (arXiv)

Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text. Current state-of-the-art methods leverage position embeddings to capture the relative position of a word to the target. However, the performance of these methods depends on the ability to incorporate this information into word representations. In this paper, we explore a variety of text encoders based on pretrained word embeddings or language models that leverage part-of-speech and position embeddings, aiming to examine the actual contribution of each component in TOWE. We also adapt a graph convolutional network (GCN) to enhance word representations by incorporating syntactic information. Our experimental results demonstrate that BiLSTM-based models can effectively encode position information into word representations while using a GCN only achieves marginal gains. Interestingly, our simple methods outperform several state-of-the-art complex neural structures.