Dependency Tree Representations of Predicate-Argument Structures
Authors: Likun Qiu, Yue Zhang, Meishan Zhang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performances of two simple sequence labeling models trained on our treebank with those of a stateof-the-art SRL system on its equivalent PB-style conversion, demonstrating the effectiveness of the novel semantic representation. Table 3 shows the main results of PB-style evaluation. Table 4 shows the results of the CST-style evaluation for the MLN system. |
| Researcher Affiliation | Academia | 1School of Chinese Language and Literature, Ludong University, China 2Singapore University of Technology and Design, Singapore qiulikun@pku.edu.cn, yue zhang@sutd.edu.sg |
| Pseudocode | No | The paper describes a "transfer algorithm" but does not provide it in the form of pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | We make our treebank and the proposition generation script freely available at klcl.pku.edu.cn or www.shandongnlp.com. |
| Open Datasets | Yes | Given our framework, a semantic treebank, the Chinese Semantic Treebank (CST), containing 14,463 sentences, is constructed. This corpus is based on the Peking University Multi-view Chinese Treebank (PMT) release 1.0 (Qiu et al. 2014), which is a dependency treebank. We make our treebank and the proposition generation script freely available at klcl.pku.edu.cn or www.shandongnlp.com. |
| Dataset Splits | Yes | Sentences 12001-13000 and 13001-14463 are used as the development and test sets, respectively. The remaining sentences are used as the training data. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) are provided for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Conditional Random Fields', 'Markov Logic Network', and 'MATE-tools' with associated references and URLs, but does not specify exact version numbers for these software packages or any other programming language or library dependencies used for the experiments. |
| Experiment Setup | No | The paper describes the features used for sequence labeling in Table 2 but does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs, optimizer settings) or detailed training configurations. |