Negation scope resolution is the task that identifies the part of a sentence affected by the negation cue. The three major corpora used for this task, the BioScope corpus, the SFU review corpus and the Sherlock dataset, have different annotation schemes for negation scope. Due to the different annotations, the negation scope resolution models based on pre-trained language models (PLMs) perform worse when fine-tuned on the simply combined dataset consisting of the three corpora. To address this issue, we propose a method for automatically converting the scopes of BioScope and SFU to those of Sherlock and merge them into a unified dataset. To verify the effectiveness of the proposed method, we conducted experiments using the unified dataset for fine-tuning PLM-based models. The experimental results demonstrate that the performances of the models increase when fine-tuned on the unified dataset unlike the simply combined one. In the token-level metric, the model fine-tuned on the unified dataset archived the state-of-the-art performance on the Sherlock dataset.

This paper proposes a new representation for CCG derivations.CCG derivations are represented as trees whose nodes are labeled with categories strictly restricted by CCG rule schemata.This characteristic is not suitable for span-based parsing models because they predict node labels independently.In other words, span-based models may generate invalid CCG derivations that violate the rule schemata.Our proposed representation decomposes CCG derivations into several independent pieces and prevents the span-based parsing models from violating the schemata.Our experimental result shows that an off-theshelf span-based parser with our representation is comparable with previous CCG parsers.

A gapping construction consists of a coordinated structure where redundant elements are elided from all but one conjuncts.This paper proposes a method of parsing sentences with gapping to recover elided elements.The proposed method is based on constituent trees annotated with grammatical and semantic roles that are useful for identifying elided elements.Our method outperforms the previous method in terms of F-measure and recall.

The Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies.This paper proposes a method that approximates PTB graph-structured representations by trees.By our approximation method, we can reduce nonlocal dependency identification and constituency parsing into single treebased parsing.An experimental result demonstrates that our approximation method with an off-the-shelf tree-based constituency parser significantly outperforms the previous methods in nonlocal dependency identification.

This paper proposes a left-corner parser which can identify nonlocal dependencies.Our parser integrates nonlocal dependency identification into a transition-based system.We use a structured perceptron which enables our parser to utilize global features captured by nonlocal dependencies.An experimental result demonstrates that our parser achieves a good balance between constituent parsing and nonlocal dependency identification.

Towards a real-time spoken dialogue system, several incremental parsing methods have been proposed so far. They constructs syntactic structures for an initial fragment of an input sentence. However, they have a problem that the structures do not necessarily represent the syntactic relation correctly. The problem is caused by the ambiguity of initial fragments. This paper proposes an incremental disambiguation method, which decides correct structures at an stage where the entire input is not completed. The method finds the structures which are correct independently of the remaining input. When correct structures cannot be decided, the method delays the decision. Since the disambiguation is executed for every word input, the method can find correct structures at an early stage.