Slogans Are Not Forever: Adapting Linguistic Expressions to the News
Authors: Lorenzo Gatti, Gözde özbal, Marco Guerini, Oliviero Stock, Carlo Strapparava
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation that we carried out confirms the effectiveness of our approach for evoking a novel concept by modifying these well known expressions. |
| Researcher Affiliation | Academia | Lorenzo Gatti FBK-irst Trento, Italy l.gatti@fbk.eu Gozde Ozbal FBK-irst Trento, Italy gozbalde@gmail.com Marco Guerini Trento RISE Trento, Italy marco.guerini@trentorise.eu Oliviero Stock FBK-irst Trento, Italy stock@fbk.eu Carlo Strapparava FBK-irst Trento, Italy strappa@fbk.eu |
| Pseudocode | No | The paper describes the system components and their processes but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodology described, nor does it state that the code is publicly available. |
| Open Datasets | Yes | Each probability of a lemma is calculated as the number of headlines that it appears divided by the total number of headlines occurring in the corpus. Then, we tokenize and Po S-tag the daily snippets with Stanford Parser [Klein and Manning, 2003] and lemmatize them with Word Net. ... LDC Giga Word 5th Edition corpus4 (http://www.ldc.upenn.edu/Catalog/catalog Entry.jsp?catalog Id= LDC2011T07) |
| Dataset Splits | No | The paper describes using a similarity threshold ('The similarity filter was set to cos > 0.6. Threshold values were determined empirically.') and a crowdsourced evaluation on '16 pairs of sentences for each strategy'. However, it does not specify explicit training/validation/test splits for model development or evaluation in the traditional machine learning sense. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions several tools and resources like 'Stanford Parser [Klein and Manning, 2003]', 'Word Net', 'Stanford Named Entity Recognizer [Finkel et al., 2005]', 'Free Base [Bollacker et al., 2008]', 'skip-gram model [Mikolov et al., 2013]', and 'Morpho Pro [Pianta et al., 2008]'. However, it does not specify version numbers for these software components or any programming languages/libraries used for implementation. |
| Experiment Setup | Yes | The system takes up to 10 most similar articles among the ones with a similarity value greater than a given threshold9. The similarity filter was set to cos > 0.6. Threshold values were determined empirically. |