Efficient Dependency-Guided Named Entity Recognition
Authors: Zhanming Jie, Aldrian Muis, Wei Lu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we show that our proposed novel dependency-guided NER model performs competitively with models based on conventional semi-Markov conditional random fields, while requiring significantly less running time. |
| Researcher Affiliation | Academia | Zhanming Jie, Aldrian Obaja Muis, Wei Lu Singapore University of Technology and Design 8 Somapah Road, Singapore, 487372 zhanming_jie@mymail.sutd.edu.sg, {aldrian_muis,luwei}@sutd.edu.sg |
| Pseudocode | No | The paper presents mathematical equations and descriptions of models, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make our code and system available for download at http://statnlp.org/research/ie/. |
| Open Datasets | Yes | For experiments, we followed (Finkel and Manning 2009) and used the Broadcast News section of the Onto Notes dataset. Instead of using its earlier 2.0 release, we used the final release release 5.0 of the dataset, which is available for download1. and 1https://catalog.ldc.upenn.edu/LDC2013T19/ |
| Dataset Splits | Yes | Following (Finkel and Manning 2009), we split the first 75% of the data for training and performed evaluations on the remaining 25%. and developed the L2 coefficient using cross-validation (see supplementary material S.1 for details). |
| Hardware Specification | No | The paper discusses training and running times but does not specify any hardware details (e.g., CPU/GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'Stanford Core NLP', 'Malt Parser', and 'Stanford dependency parser' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We set L = 8, which can cover all entities in our dataset, and developed the L2 coefficient using cross-validation (see supplementary material S.1 for details). and The features used in this paper are basic features which are commonly used in linear-chain CRFs and semi-CRFs. For the linear-chain CRFs, we consider the current word/POS tag, the previous word/POS tag, the current word shape, the previous word shape, prefix/suffix of length up to 3, as well as transition features. |