Deep Semantic Parsing with Upper Ontologies

2021 ◽  
Vol 11 (20) ◽  
pp. 9423
Author(s):  
Algirdas Laukaitis ◽  
Egidijus Ostašius ◽  
Darius Plikynas
Keyword(s):  
Virtual Reality ◽  
State Of The Art ◽  
Ontology Mapping ◽  
New Method ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Semantic Parsing ◽  
Semantic Roles ◽  
Future Studies ◽  
Upper Ontology

This paper presents a new method for semantic parsing with upper ontologies using FrameNet annotations and BERT-based sentence context distributed representations. The proposed method leverages WordNet upper ontology mapping and PropBank-style semantic role labeling and it is designed for long text parsing. Given a PropBank, FrameNet and WordNet-labeled corpus, a model is proposed that annotates the set of semantic roles with upper ontology concept names. These annotations are used for the identification of predicates and arguments that are relevant for virtual reality simulators in a 3D world with a built-in physics engine. It is shown that state-of-the-art results can be achieved in relation to semantic role labeling with upper ontology concepts. Additionally, a manually annotated corpus was created using this new method and is presented in this study. It is suggested as a benchmark for future studies relevant to semantic parsing.

scholarly journals Syntax-Aware Neural Semantic Role Labeling

2019 ◽  
Vol 33 ◽  
pp. 7305-7313 ◽  
Author(s):  
Qingrong Xia ◽  
Zhenghua Li ◽  
Min Zhang ◽  
Meishan Zhang ◽  
Guohong Fu ◽  
...  
Keyword(s):  
Close Correlation ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Semantic Parsing ◽  
Single Model ◽  
Improve Performance ◽  
Syntactic Structures ◽  
Word Sequence ◽  
Input Sentence ◽  
Feature Based

Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic structures, traditional discrete-feature-based SRL approaches make heavy use of syntactic features. In contrast, deep-neural-network-based approaches usually encode the input sentence as a word sequence without considering the syntactic structures. In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are beneficial for neural SRL models. Experiments on the benchmark CoNLL-2005 dataset show that syntax-aware SRL approaches can effectively improve performance over a strong baseline with external word representations from ELMo. With the extra syntax-aware representations, our approaches achieve new state-of-the-art 85.6 F1 (single model) and 86.6 F1 (ensemble) on the test data, outperforming the corresponding strong baselines with ELMo by 0.8 and 1.0, respectively. Detailed error analysis are conducted to gain more insights on the investigated approaches.

scholarly journals Context-aware Frame-Semantic Role Labeling

2015 ◽  
Vol 3 ◽  
pp. 449-460 ◽  
Author(s):  
Michael Roth ◽  
Mirella Lapata
Keyword(s):  
Social Network ◽  
Question Answering ◽  
State Of The Art ◽  
Semantic Role ◽  
Context Aware ◽  
Semantic Role Labeling ◽  
Current State ◽  
Sentence Level ◽  
Small Set ◽  
Labeling System

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.

scholarly journals A Global Joint Model for Semantic Role Labeling

2008 ◽  
Vol 34 (2) ◽  
pp. 161-191 ◽  
Author(s):  
Kristina Toutanova ◽  
Aria Haghighi ◽  
Christopher D. Manning
Keyword(s):  
State Of The Art ◽  
Joint Model ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Shared Task ◽  
Data Set ◽  
Joint Structure ◽  
Proposed Model ◽  
Parse Trees ◽  
Linguistic Intuition

We present a model for semantic role labeling that effectively captures the linguistic intuition that a semantic argument frame is a joint structure, with strong dependencies among the arguments. We show how to incorporate these strong dependencies in a statistical joint model with a rich set of features over multiple argument phrases. The proposed model substantially outperforms a similar state-of-the-art local model that does not include dependencies among different arguments. We evaluate the gains from incorporating this joint information on the Propbank corpus, when using correct syntactic parse trees as input, and when using automatically derived parse trees. The gains amount to 24.1% error reduction on all arguments and 36.8% on core arguments for gold-standard parse trees on Propbank. For automatic parse trees, the error reductions are 8.3% and 10.3% on all and core arguments, respectively. We also present results on the CoNLL 2005 shared task data set. Additionally, we explore considering multiple syntactic analyses to cope with parser noise and uncertainty.

scholarly journals Generalization of Semantic Roles in Automatic Semantic Role Labeling

2014 ◽  
Vol 21 (4) ◽  
pp. 841-875
Author(s):  
Yuichiroh Matsubayashi ◽  
Naoaki Okazaki ◽  
Jun’ichi Tsujii
Keyword(s):  
Semantic Role ◽  
Semantic Role Labeling ◽  
Semantic Roles

scholarly journals Labeling Chinese Predicates with Semantic Roles

2008 ◽  
Vol 34 (2) ◽  
pp. 225-255 ◽  
Author(s):  
Nianwen Xue
Keyword(s):  
Gold Standard ◽  
Argument Structure ◽  
High Performance ◽  
State Of The Art ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Pos Tagging ◽  
Fully Automatic ◽  
Syntactic Annotation ◽  
Predicate Argument Structure

In this article we report work on Chinese semantic role labeling, taking advantage of two recently completed corpora, the Chinese PropBank, a semantically annotated corpus of Chinese verbs, and the Chinese Nombank, a companion corpus that annotates the predicate-argument structure of nominalized predicates. Because the semantic role labels are assigned to the constituents in a parse tree, we first report experiments in which semantic role labels are automatically assigned to hand-crafted parses in the Chinese Treebank. This gives us a measure of the extent to which semantic role labels can be bootstrapped from the syntactic annotation provided in the treebank. We then report experiments using automatic parses with decreasing levels of human annotation in the input to the syntactic parser: parses that use gold-standard segmentation and POS-tagging, parses that use only gold-standard segmentation, and fully automatic parses. These experiments gauge how successful semantic role labeling for Chinese can be in more realistic situations. Our results show that when hand-crafted parses are used, semantic role labeling accuracy for Chinese is comparable to what has been reported for the state-of-the-art English semantic role labeling systems trained and tested on the English PropBank, even though the Chinese PropBank is significantly smaller in size. When an automatic parser is used, however, the accuracy of our system is significantly lower than the English state of the art. This indicates that an improvement in Chinese parsing is critical to high-performance semantic role labeling for Chinese.

scholarly journals Selectional Preferences for Semantic Role Classification

2013 ◽  
Vol 39 (3) ◽  
pp. 631-663 ◽  
Author(s):  
Beñat Zapirain ◽  
Eneko Agirre ◽  
Lluís Màrquez ◽  
Mihai Surdeanu
Keyword(s):  
Error Analysis ◽  
State Of The Art ◽  
Error Reduction ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Syntactic Information ◽  
Open Issue ◽  
Selectional Preferences ◽  
Post Hoc ◽  
Labeling System

This paper focuses on a well-known open issue in Semantic Role Classification (SRC) research: the limited influence and sparseness of lexical features. We mitigate this problem using models that integrate automatically learned selectional preferences (SP). We explore a range of models based on WordNet and distributional-similarity SPs. Furthermore, we demonstrate that the SRC task is better modeled by SP models centered on both verbs and prepositions, rather than verbs alone. Our experiments with SP-based models in isolation indicate that they outperform a lexical baseline with 20 F1 points in domain and almost 40 F1 points out of domain. Furthermore, we show that a state-of-the-art SRC system extended with features based on selectional preferences performs significantly better, both in domain (17% error reduction) and out of domain (13% error reduction). Finally, we show that in an end-to-end semantic role labeling system we obtain small but statistically significant improvements, even though our modified SRC model affects only approximately 4% of the argument candidates. Our post hoc error analysis indicates that the SP-based features help mostly in situations where syntactic information is either incorrect or insufficient to disambiguate the correct role.

scholarly journals Syntax-aware Semantic Role Labeling without Parsing

2019 ◽  
Vol 7 ◽  
pp. 343-356 ◽  
Author(s):  
Rui Cai ◽  
Mirella Lapata
Keyword(s):  
State Of The Art ◽  
The State ◽  
Benchmark Dataset ◽  
Experimental Results ◽  
Training Data ◽  
Data Dependency ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Syntactic Information

In this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser. The backbone of our model is an LSTM-based semantic role labeler jointly trained with two auxiliary tasks: predicting the dependency label of a word and whether there exists an arc linking it to the predicate. The auxiliary tasks provide syntactic information that is specific to semantic role labeling and are learned from training data (dependency annotations) without relying on existing dependency parsers, which can be noisy (e.g., on out-of-domain data or infrequent constructions). Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.

scholarly journals Generating Senses and RoLes: An End-to-End Model for Dependency- and Span-based Semantic Role Labeling

2021 ◽  
Author(s):  
Rexhina Blloshmi ◽  
Simone Conia ◽  
Rocco Tripodi ◽  
Roberto Navigli
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Great Success ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Complex Predicate ◽  
Input Sentence ◽  
End To End

Despite the recent great success of the sequence-to-sequence paradigm in Natural Language Processing, the majority of current studies in Semantic Role Labeling (SRL) still frame the problem as a sequence labeling task. In this paper we go against the flow and propose GSRL (Generating Senses and RoLes), the first sequence-to-sequence model for end-to-end SRL. Our approach benefits from recently-proposed decoder-side pretraining techniques to generate both sense and role labels for all the predicates in an input sentence at once, in an end-to-end fashion. Evaluated on standard gold benchmarks, GSRL achieves state-of-the-art results in both dependency- and span-based English SRL, proving empirically that our simple generation-based model can learn to produce complex predicate-argument structures. Finally, we propose a framework for evaluating the robustness of an SRL model in a variety of synthetic low-resource scenarios which can aid human annotators in the creation of better, more diverse, and more challenging gold datasets. We release GSRL at github.com/SapienzaNLP/gsrl.

scholarly journals Automatic Labeling of Semantic Roles

2002 ◽  
Vol 28 (3) ◽  
pp. 245-288 ◽  
Author(s):  
Daniel Gildea ◽  
Daniel Jurafsky
Keyword(s):  
Clustering Algorithms ◽  
Training Data ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Grammatical Function ◽  
Semantic Roles ◽  
Prior Probabilities ◽  
Statistical Classifiers ◽  
Combination Methods ◽  
Semantic Labeling

We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Given an input sentence and a target word and frame, the system labels constituents with either abstract semantic roles, such as Agent or Patient, or more domain-specific semantic roles, such as Speaker, Message, and Topic. The system is based on statistical classifiers trained on roughly 50,000 sentences that were hand-annotated with semantic roles by the FrameNet semantic labeling project. We then parsed each training sentence into a syntactic tree and extracted various lexical and syntactic features, including the phrase type of each constituent, its grammatical function, and its position in the sentence. These features were combined with knowledge of the predicate verb, noun, or adjective, as well as information such as the prior probabilities of various combinations of semantic roles. We used various lexical clustering algorithms to generalize across possible fillers of roles. Test sentences were parsed, were annotated with these features, and were then passed through the classifiers. Our system achieves 82% accuracy in identifying the semantic role of presegmented constituents. At the more difficult task of simultaneously segmenting constituents and identifying their semantic role, the system achieved 65% precision and 61% recall. Our study also allowed us to compare the usefulness of different features and feature combination methods in the semantic role labeling task. We also explore the integration of role labeling with statistical syntactic parsing and attempt to generalize to predicates unseen in the training data.

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