Solving and Explaining Analogy Questions Using Semantic Networks
Authors: Adrian Boteanu, Sonia Chernova
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on two datasets totaling 600 analogy questions. Our results show reliable performance and low false-positive rate in question answering; human evaluators agreed with 96% of our analogy explanations. |
| Researcher Affiliation | Academia | Adrian Boteanu, Sonia Chernova Worcester Polytechnic Institute 100 Institute Road Worcester, MA 01609 aboteanu@wpi.edu, soniac@wpi.edu |
| Pseudocode | Yes | Algorithm 1 Sequence similarity. s1 and s2 have the same length, each connecting a pair of concepts in different contexts. ... Algorithm 2 Generating a human-readable explanation from the best similarity sequence pair, which have the same length. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | 373 questions used in SAT US college admittance tests. This dataset was also used in previous work on answering analogies (Turney and Littman 2005); ... 227 questions from a public domain website1 targeted for grades 1-12. ... Footnote 1: Section Unit 2: Read Theory Word Pair Analogies from http://www.englishforeveryone.org/Topics/Analogies.htm |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, counts) for training, validation, and test sets. It evaluates on pre-existing datasets of analogy questions. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Concept Net, Word Net, Verb Net, DBPedia, and Divisi toolkit, but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | In our analysis, we limited the semantic context subgraphs to have a maximum geodesic distance of two. ... We conducted our survey through the Crowd Flower crowdsourcing market using the 74 explanations (60 direct, 14 one-hop) produced by SSE from the correct answers selected when ignoring Related To and Conceptually Related To edges. |