| Challenge: | a challenge for aspect term extraction is to extract phrase-level aspect terms . a constituency lattice structure is constructed using the span annotations of constituents of a sentence . |
| Approach: | They propose to incorporate the span annotations of constituents of a sentence to leverage syntactic information in neural network models. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets. |
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Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)
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| Challenge: | Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. |
| Approach: | They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances. |
| Outcome: | The proposed model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, surpassing the previous single model results by a large margin. |
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(nˆ6) down to O(nˆ3) (2020.emnlp-main)
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| Challenge: | a novel chart-based parser for discontinuous constituency trees is proposed for span-based span parsing . it can process discontinuous constituent trees of block degree two, including ill-nested structures . |
| Approach: | They propose a chart-based algorithm for span-based parsing of discontinuous constituency trees . they build variants with smaller search spaces and time complexities ranging from O(n6) down to O(N3) . |
| Outcome: | The proposed algorithm can process 98% of constituents in linguistic treebanks while having the same complexity as continuous constituency parsers. |
Improving Constituency Parsing with Span Attention (2020.findings-emnlp)
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| Challenge: | Constituency parsing is a fundamental task for natural language understanding . n-grams are a conventional type of feature for contextual information . experimental results show that neural parsers with no grammar rules outperform statistical ones . |
| Approach: | They propose to incorporate n-grams into span representations by weighting them according to their contributions to the parsing process. |
| Outcome: | The proposed approach outperforms existing statistical grammar-based models on Arabic, Chinese, and English datasets. |
Constituency Parsing with a Self-Attentive Encoder (P18-1)
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| Challenge: | Recent work on LSTM encoders based on recurrent neural networks has led to improvements in constituency parsing accuracy. |
| Approach: | They propose to replace an LSTM encoder with a self-attentive architecture to improve a discriminative constituency parser. |
| Outcome: | The proposed model outperforms the previous best-published results on 8 of the 9 languages in the SPMRL dataset. |
What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
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| Challenge: | a number of differences have emerged between classical and modern constituency parsing approaches . structural components like grammars and feature-rich lexicons are becoming less central . recurrent neural networks have gained traction as a powerful and general purpose tool for representation . |
| Approach: | They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past. |
| Outcome: | The proposed model outperforms state-of-the-art models under similar conditions. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |
Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)
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| Challenge: | Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions. |
| Approach: | They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level. |
| Outcome: | The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency . |
An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
| Approach: | They propose to build a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. |
| Outcome: | The proposed model outperforms existing models in natural language generation tasks without any explicit task-specific knowledge or architecture of constituent parsing. |
Challenges to Open-Domain Constituency Parsing (2022.findings-acl)
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| Challenge: | Existing findings on cross-domain constituency parsing are only made on a limited number of domains. |
| Approach: | They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features. |
| Outcome: | The proposed model significantly improves the performance of the proposed model on the domain-variant features. |
Parsing Headed Constituencies (2024.lrec-main)
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| Challenge: | Using constituency and dependency trees, syntactic representations are preferred for tasks such as nominal phrase extraction and identification of terminology. |
| Approach: | They propose a parsing technique that generates headed constituency trees which combine information typically contained in constituency and dependency trees. |
| Outcome: | The proposed method generates headed constituency trees with discontinuities and can generate constituency tree with discontinuity. |