CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks

Foland, William; Martin, James H.

doi:10.18653/v1/s16-1185

Cited by 11 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6.2.7 CU-NLP (Foland and Martin, 2016) This parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for estimating AMR graph structure.…”

Section: Uofrmentioning

confidence: 99%

SemEval-2016 Task 8: Meaning Representation Parsing

May¹

2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

View full text Add to dashboard Cite

In this report we summarize the results of the SemEval 2016 Task 8: Meaning Representation Parsing. Participants were asked to generate Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the news and discussion forum domains. Eleven sites submitted valid systems. The availability of state-of-the-art baseline systems was a key factor in lowering the bar to entry; many submissions relied on CAMR (Wang et al., 2015b; Wang et al., 2015a) as a baseline system and added extensions to it to improve scores. The evaluation set was quite difficult to parse, particularly due to creative approaches to word representation in the web forum portion. The top scoring systems scored 0.62 F1 according to the Smatch (Cai and Knight, 2013) evaluation heuristic. We show some sample sentences along with a comparison of system parses and perform quantitative ablative studies.

show abstract

Section: Uofrmentioning

confidence: 99%

SemEval-2016 Task 8: Meaning Representation Parsing

May¹

2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

View full text Add to dashboard Cite

show abstract

“…More recently, Foland and Martin (2016; describe a neural network based model that decomposes the AMR parsing task into a series of subproblems. Their system first identifies the concepts using a Bidirectional LSTM Recurrent Neural Network (Hochreiter and Schmidhuber, 1997), and then locates and labels the arguments and attributes for each predicate, and finally constructs the AMR using the concepts and relations identified in previous steps.…”

Section: Related Workmentioning

confidence: 99%

“…While it is trivial to categorize the PREDICATE, NON-PREDICATE, CONST cases, there is no straightforward way to deal with the MULTICONCEPT type. Foland and Martin (2016) only handle named entities, which constitute the Based on the observation that many of the MULTICONCEPT cases are actually similarly structured subgraphs that only differ in the lexical items, we choose to factor the lexical items out of the subgraph fragments and use the skeletal structure as the fine-grained labels, which we refer as Factored Concept Label (FCL). Figure 4 shows that although English words "visitor" and "worker" have been aligned to different subgraph fragments, after replacing the lexical items, in this case the leaf concepts visit-01 and work-01 with a placeholder "x", we are able to arrive at the same FCL.…”

Section: Factored Concept Labelsmentioning

confidence: 99%

See 1 more Smart Citation

Getting the Most out of AMR Parsing

Wang

Xue

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.

show abstract

“…In this trend, the task of AMR has been held continuously for two years in SemEval-2016 andSemEval-2017. There have been many parsers shown its outstanding performance for high Fscore points like RIGA (Barzdins and Gosko, 2016), CAMR, CU-NLP (Foland and Martin, 2016), etc.…”

Section: Introductionmentioning

confidence: 99%

UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR

Nguyen¹,

Nguyen

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

View full text Add to dashboard Cite

This paper describes the improvements that we have applied on CAMR baseline parser (Wang et al., 2016) at Task 8 of SemEval-2016. Our objective is to increase the performance of CAMR when parsing sentences from scientific articles, especially articles of biology domain more accurately. To achieve this goal, we built two wrapper layers for CAMR. The first layer, which covers the input data, will normalize, add necessary information to the input sentences to make the input dependency parser and the aligner better handle reference citations, scientific figures, formulas, etc. The second layer, which covers the output data, will modify and standardize output data based on a list of scientific concept fixing patterns. This will help CAMR better handle biological concepts which are not in the training dataset. Finally, after applying our approach, CAMR has scored 0.65 F-score 1 on the test set of Biomedical training data 2 and 0.61 F-score on the official blind test dataset.

show abstract

CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks

Abstract: We describe the system used in our participation in the AMR Parsing task for SemEval-2016. Our parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for estimating AMR graph structure.

Cited by 11 publications

References 12 publications

SemEval-2016 Task 8: Meaning Representation Parsing

SemEval-2016 Task 8: Meaning Representation Parsing

Getting the Most out of AMR Parsing

UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR

Contact Info

Product

Resources

About