LangPro: Natural Language Theorem Prover

By Lasha Abzianidze. 2017

42 citations recorded by the source corpus.

LangPro is an automated theorem prover for natural language. Given a set of premises and a hypothesis, it is able to prove semantic relations between them. The prover is based on a version of analytic tableau method specially designed for natural logic. The proof procedure operates on logical forms that preserve linguistic expressions to a large extent. %This property makes the logical forms easily obtainable from syntactic trees. %, in particular, Combinatory Categorial Grammar derivation trees. The nature of proofs is deductive and transparent. On the FraCaS and SICK textual entailment datasets, the prover achieves high results comparable to state-of-the-art.

Subfields: Artificial Intelligence, Prompting & Reasoning

How researchers see this paper

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

In Artificial Intelligence

1. Kyle Richardson
2. Andreas Vlachos
3. Sebastian Riedel
4. Hai Hu
5. Larry Moss
6. Amrith Krishna
7. Sandra Kübler
8. Mohit Bansal

In Prompting & Reasoning

1. Andreas Vlachos
2. Hai Hu
3. Qi Chen
4. Larry Moss
5. Amrith Krishna
6. Kyle Richardson
7. Sebastian Riedel

Citations in the corpus

• ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, methodology): “…Lakoff (1972), has been previously employed in several information extraction and NLU tasks such as Natural Language Inference (NLI, Angeli and Manning, 2014; Abzianidze, 2017a; Feng et al., 2020), question answering (Angeli et al., 2016) and open information extraction (Angeli, 2016, Chapter 5).”
• MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity (2019, background): “In parallel to these efforts, there have also been recent logic-based approaches to NLI ( Mineshima et al., 2015; Mart´ınez-G´omez et al., 2016; Mart´ınez-G´omez et al., 2017; Abzianidze, 2017; Yanaka et al., 2018), which take inspiration from linguistics.”
• Natural Language Inference with Monotonicity (2019, background): “This paper is a contribution to the third category, as are Abzianidze (2015, 2017); MacCartney and Manning (2009); Angeli and Manning (2014); Angeli et al. (2016); Hu et al. (2018); Mineshima et al. (2015).”