Automatically Creating Multilingual Lexical Resources
Authors: Khang Lam
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary results We have experimented with a few languages as proof of concept of our ideas and algorithms (Lam and Kalita 2013) and (Lam, Tarouti, and Kalita 2014b). The dictionaries we create using the DRw S algorithm with the sim Value of 1.0 are the best.We have created 48 new bilingual dictionaries, out of which 30 pairs of languages are not supported by MT, from 5 existing dictionaries. We have also experimented with endangered languages to evaluate our work (Lam, Tarouti, and Kalita 2014b). |
| Researcher Affiliation | Academia | Khang Nhut Lam University of Colorado 1420 Austin Bluffs Pkwy Colorado Springs, CO USA 80918 |
| Pseudocode | No | The paper describes algorithms such as 'Direct Reversal' and 'Direct Reversal with Similarity' but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing their code for the work described in this paper, nor does it provide a direct link to a source-code repository for their methodology. It mentions 'Microsoft Translator' with a link, but this is a third-party tool. |
| Open Datasets | No | The paper refers to previous work (Lam and Kalita 2013, Lam, Tarouti, and Kalita 2014b) and the Princeton Wordnet (PWN), but it does not provide concrete access information (specific link, DOI, repository name, or formal citation for a specific dataset with authors/year for the data itself) for a publicly available or open dataset used in its experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information, such as exact percentages, sample counts for training, validation, or testing, or citations to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Microsoft Translator' as a tool used, but it does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes algorithmic details and compares different approaches (e.g., 'the DRw S algorithm with the sim Value of 1.0'), but it does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings commonly found in research papers for reproducibility. |