Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on
Chinese Comment Text
- URL: http://arxiv.org/abs/2108.00400v1
- Date: Sun, 1 Aug 2021 08:42:26 GMT
- Title: Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on
Chinese Comment Text
- Authors: Binlong Zhang, Wei Zhou
- Abstract summary: We propose T-E-GRU for Chinese sentiment analysis, which combine transformer encoder and GRU.
In view of the confusion of punctuation marks in Chinese comment texts, we selectively retain some punctuation marks with sentence segmentation ability.
- Score: 4.23836170294948
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Chinese sentiment analysis (CSA) has always been one of the challenges in
natural language processing due to its complexity and uncertainty. Transformer
has succeeded in capturing semantic features, but it uses position encoding to
capture sequence features, which has great shortcomings compared with the
recurrent model. In this paper, we propose T-E-GRU for Chinese sentiment
analysis, which combine transformer encoder and GRU. We conducted experiments
on three Chinese comment datasets. In view of the confusion of punctuation
marks in Chinese comment texts, we selectively retain some punctuation marks
with sentence segmentation ability. The experimental results show that T-E-GRU
outperforms classic recurrent model and recurrent model with attention.
Related papers
- Word Order in English-Japanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translation [13.713981533436135]
This paper analyzes the features of monotonic translations, which follow the word order of the source language, in simultaneous interpreting (SI)
We analyzed the characteristics of chunk-wise monotonic translation (CMT) sentences using the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation dataset.
We further investigated the features of CMT sentences by evaluating the output from the existing speech translation (ST) and simultaneous speech translation (simulST) models on the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation dataset.
arXiv Detail & Related papers (2024-06-13T09:10:16Z) - Chinese Financial Text Emotion Mining: GCGTS -- A Character
Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction [7.484918031250864]
Aspect-Opinion Pair Extraction (AOPE) from Chinese financial texts is a specialized task in fine-grained text sentiment analysis.
Previous studies have mainly focused on developing grid annotation schemes within grid-based models to facilitate this extraction process.
We propose a novel method called Graph-based Character-level Grid Tagging Scheme (GCGTS)
The GCGTS method explicitly incorporates syntactic structure using Graph Convolutional Networks (GCN) and unifies the encoding of characters within the same semantic unit (Chinese word level)
arXiv Detail & Related papers (2023-08-04T02:20:56Z) - Discourse Representation Structure Parsing for Chinese [8.846860617823005]
We explore the feasibility of Chinese semantic parsing in the absence of labeled data for Chinese meaning representations.
We propose a test suite designed explicitly for Chinese semantic parsing, which provides fine-grained evaluation for parsing performance.
Our experimental results show that the difficulty of Chinese semantic parsing is mainly caused by adverbs.
arXiv Detail & Related papers (2023-06-16T09:47:45Z) - Can Transformer be Too Compositional? Analysing Idiom Processing in
Neural Machine Translation [55.52888815590317]
Unlike literal expressions, idioms' meanings do not directly follow from their parts.
NMT models are often unable to translate idioms accurately and over-generate compositional, literal translations.
We investigate whether the non-compositionality of idioms is reflected in the mechanics of the dominant NMT model, Transformer.
arXiv Detail & Related papers (2022-05-30T17:59:32Z) - Multilingual Extraction and Categorization of Lexical Collocations with
Graph-aware Transformers [86.64972552583941]
We put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context.
Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
arXiv Detail & Related papers (2022-05-23T16:47:37Z) - TransCouplet:Transformer based Chinese Couplet Generation [1.084959821967413]
Chinese couplet is a form of poetry composed of complex syntax with ancient Chinese language.
This paper presents a transformer-based sequence-to-sequence couplet generation model.
We also evaluate the Glyph, PinYin and Part-of-Speech tagging on the couplet grammatical rules.
arXiv Detail & Related papers (2021-12-03T04:34:48Z) - Investigating the Reordering Capability in CTC-based Non-Autoregressive
End-to-End Speech Translation [62.943925893616196]
We study the possibilities of building a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC)
CTC's success on translation is counter-intuitive due to its monotonicity assumption, so we analyze its reordering capability.
Our analysis shows that transformer encoders have the ability to change the word order.
arXiv Detail & Related papers (2021-05-11T07:48:45Z) - Character-Level Translation with Self-attention [9.864260997723974]
We explore the suitability of self-attention models for character-level neural machine translation.
We test the standard transformer model and a novel variant in which the encoder block combines information from nearby characters using convolutions.
Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments.
arXiv Detail & Related papers (2020-04-30T14:05:26Z) - Self-Attention with Cross-Lingual Position Representation [112.05807284056337]
Position encoding (PE) is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences.
Due to word order divergences in different languages, modeling the cross-lingual positional relationships might help SANs tackle this problem.
We augment SANs with emphcross-lingual position representations to model the bilingually aware latent structure for the input sentence.
arXiv Detail & Related papers (2020-04-28T05:23:43Z) - Explicit Reordering for Neural Machine Translation [50.70683739103066]
In Transformer-based neural machine translation (NMT), the positional encoding mechanism helps the self-attention networks to learn the source representation with order dependency.
We propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT.
The empirical results on the WMT14 English-to-German, WAT ASPEC Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the effectiveness of the proposed approach.
arXiv Detail & Related papers (2020-04-08T05:28:46Z) - Sign Language Transformers: Joint End-to-end Sign Language Recognition
and Translation [59.38247587308604]
We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation.
We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T dataset.
Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models.
arXiv Detail & Related papers (2020-03-30T21:35:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.