Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction
Authors: Yichun Yin, Furu Wei, Li Dong, Kaimeng Xu, Ming Zhang, Ming Zhou
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Sem Eval datasets show that, (1) with only embedding features, we can achieve state-of-the-art results; (2) our embedding method which incorporates the syntactic information among words yields better performance than other representative ones in aspect term extraction. |
| Researcher Affiliation | Collaboration | Yichun Yin1, Furu Wei2, Li Dong3, Kaimeng Xu1, Ming Zhang1 , Ming Zhou2 1School of EECS, Peking University 2Microsoft Research 3Institute for Language, Cognition and Computation, University of Edinburgh |
| Pseudocode | No | The paper describes the model training and feature construction using natural language and mathematical equations, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides links to the implementations of *baseline* models used for comparison but does not state that the code for their own proposed methodology is publicly released or provide a link to it. |
| Open Datasets | Yes | We conduct our experiments on the Sem Eval 2014 and 2015 datasets. The corpora contain Yelp dataset3 and Amazon dataset4 which are in-domain corpora for restaurant domain and laptop domain respectively. 3https://www.yelp.com/academic dataset 4https://snap.stanford.edu/data/web-Amazon.html |
| Dataset Splits | Yes | In order to choose l and d, we use 80% sentences in training data as training set, and the rest 20% as development set. |
| Hardware Specification | No | The paper mentions 'asynchronous gradient descent for parallel training' but does not provide specific details on the hardware used, such as GPU/CPU models, memory, or processing units. |
| Software Dependencies | No | The paper mentions using 'Stanford corenlp' and 'an available CRF tool' (crfsharp.codeplex.com) but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The dimensions of word and dependency path embeddings are set to 100. Larger dimensions get similar results in the development set but need more training time. l is set to 15 that performs best in the development set. We use asynchronous gradient descent for parallel training. Following the strategy for updating learning rate [Mikolov et al., 2013a], we linearly decrease it over our training instances. The initial learning rate is set to 0.001. |