Semantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling. Previous studies usually formulate the entire SRL problem into two or more subtasks. For the first time, this paper introduces an end-to-end neural model which unifiedly tackles the predicate disambiguation and the argument labeling in one shot. Using a biaffine scorer, our model directly predicts all semantic role labels for all given word pairs in the sentence without relying on any syntactic parse information. Specifically, we augment the BiLSTM encoder with a non-linear transformation to further distinguish the predicate and the argument in a given sentence, and model the semantic role labeling process as a word pair classification task by employing the biaffine attentional mechanism. Though the proposed model is syntax-agnostic with local decoder, it outperforms the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009 benchmarks for both English and Chinese. To our best knowledge, we report the first syntax-agnostic SRL model that surpasses all known syntax-aware models.
However, almost all of previous works treated the predicate disambiguation as individual subtasks, apart from @cite_13 @cite_30 @cite_27 @cite_8 , who presented the first end-to-end system for dependency SRL. For the neural models of dependency SRL, we have presented the first end-to-end solution that handles both semantic labeling subtasks in one single model. At the same time, our model enjoys the advantage that does not rely any syntactic information.
@cite_30 @cite_27 @cite_13 @cite_8
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This demonstration presents a high-performance syntactic and semantic dependency parser. The system consists of a pipeline of modules that carry out the to-kenization, lemmatization, part-of-speech tagging, dependency parsing, and semantic role labeling of a sentence. The system's two main components draw on improved versions of a state-of-the-art dependency parser (Bohnet, 2009) and semantic role labeler (, 2009) developed independently by the authors. The system takes a sentence as input and produces a syntactic and semantic annotation using the CoNLL 2009 format. The processing time needed for a sentence typically ranges from 10 to 1000 milliseconds. The predicate--argument structures in the final output are visualized in the form of segments, which are more intuitive for a user. This paper describes our system about multilingual semantic dependency parsing (SR-Lonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into a word-pair classification problem and implemented as a single-stage machine learning system. For each input corpus, a large scale feature engineering is conducted to select the best fit feature template set incorporated with a proper argument pruning strategy. The system achieved the top average score in the closed challenge: 80.47 semantic labeled F1 for the average score. This paper describes our system to carry out the joint parsing of syntactic and semantic dependencies for our participation in the shared task of CoNLL-2008. We illustrate that both syntactic parsing and semantic parsing can be transformed into a word-pair classification problem and implemented as a single-stage system with the aid of maximum entropy modeling. Our system ranks the fourth in the closed track for the task with the following performance on the WSJ+Brown test set: 81.44 labeled macro F1 for the overall task, 86.66 labeled attachment for syntactic dependencies, and 76.16 labeled F1 for semantic dependencies. Semantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series of submodels each specialized for a specific subtask, the one presented in this article integrates everything into one model, in hopes of achieving desirable integrity and practicality for real applications while maintaining a competitive performance. This integrative approach tackles semantic parsing as a word pair classification problem using a maximum entropy classifier. We leverage adaptive pruning of argument candidates and large-scale feature selection engineering to allow the largest feature space ever in use so far in this field, it achieves a state-of-the-art performance on the evaluation data set for CoNLL-2008 shared task, on top of all but one top pipeline system, confirming its feasibility and effectiveness.