Task-Specific Representation Learning for Web-Scale Entity Disambiguation
Authors: Rijula Kar, Susmija Reddy, Sourangshu Bhattacharya, Anirban Dasgupta, Soumen Chakrabarti
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report extensively on the accuracy of TSRL for the NED task over the standard Co NLL, MSNBC, AQUAINT and ACE data sets (Hoffart and others 2011; Guo and Barbosa 2016). In terms of both microand macro-averaged accuracy, TSRL surpasses standard MTL and MTRL approaches, as well as the best feature-engineered baselines, in most cases. |
| Researcher Affiliation | Academia | Rijula Kar,* Susmija Reddy,* Sourangshu Bhattacharya* Anirban Dasgupta, , Soumen Chakrabarti * IIT Kharagpur, India, IIT Gandhinagar, India, IIT Bombay, India rijula.cse@iitkgp.ac.in, {jsreddy,sourangshu}@cse.iitkgp.ernet.in, anirbandg@iitgn.ac.in, soumen@cse.iitb.ac.in |
| Pseudocode | No | The paper describes methods and models in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available1. 1Visit https://github.com/rijula/tsrl-aaai18 and https://goo.gl/ vw6C6g |
| Open Datasets | Yes | We report extensively on the accuracy of TSRL for the NED task over the standard Co NLL, MSNBC, AQUAINT and ACE data sets (Hoffart and others 2011; Guo and Barbosa 2016). We used the alias-entity mapping indexes created by (Ganea and Hofmann 2017)3. The training corpus was collected from the November 2016 Wikipedia dump4. |
| Dataset Splits | No | The paper mentions training on various datasets and evaluating on a 'testb test fold' but does not explicitly provide details for a separate validation split, such as percentages or sample counts, needed for full reproducibility of data partitioning. |
| Hardware Specification | Yes | All experiments were implemented in Theano 0.8.2 and run on a few Xeon servers with 32 cores and 96 GB RAM each. |
| Software Dependencies | Yes | All experiments were implemented in Theano 0.8.2 |
| Experiment Setup | Yes | For optimization, we used SGD with minibatches of 1000 mention instances and learning rate 1/k where k is the epoch number. Label predictions were made after averaging the model weights over the last 30 iterations, to remove noise. L2 regularizers were logarithmically grid-searched between 10 6 and 106, and reporting best accuracy achieved in the test dataset. |