Natural Language Processing for Solving Simple Word Problems

By Sowmya S. Sundaram, Deepak Khemani. 2015

25 citations recorded by the source corpus.

Subfields: Natural Language Processing, Question Answering & Reading Comprehension

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Lei Wang
2. Bing Tian Dai
3. Dongxiang Zhang
4. Heng Tao Shen
5. Nuo Xu
6. Jipeng Zhang
7. Wei Dang Lu
8. Zhenwen Liang

In Question Answering & Reading Comprehension

1. Lei Wang
2. Heng Tao Shen
3. Bing Tian Dai
4. Dongxiang Zhang
5. Nuo Xu

Citations in the corpus

• The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers (2018, methodology): “In contrast, ARIS [21] does not work as good because it focuses on “change” schema of quantities and does not fully exploit other schemas like “compare” [22].”
• A Weakly Supervised Model for Solving Math word Problems (2021, unknown): “On the other hand, statistics-based meth-ods (Hosseini et al., 2014; Kushman et al., 2014; Sundaram and Khemani, 2015; Mitra and Baral, 2016; Liang et al., 2016a,b) use pre-defined logic templates and employ traditional machine learning models to identify entities, quantities, and operators from the…”