Using the Framework

By Stephen G. Pulman. 1996

How researchers see this paper

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

In Software Engineering

Citations in the corpus

Analysis Methods in Neural Language Processing: A Survey (2018, background): “For instance, extending the categories in Cooper et al. (1996), the GLUE analysis set for NLI covers more than 30 phenomena in four coarse categories (lexical semantics, predicate-argument structure, logic, and knowledge).”
A SICK cure for the evaluation of compositional distributional semantic models (2014, background): “A first interesting test suite of entailment problems, FraCaS (Framework for Computational Semantics), was developed already in the mid-’90 by a group of formal semanticists (Cooper et al., 1996), but a cleanly processable version of it has been made available only recently.”
Stress Test Evaluation for Natural Language Inference (2018, background): “NLI has been proposed as a benchmark task for natural language understanding research (Cooper et al., 1996; Dagan et al., 2006; Giampiccolo et al., 2007; Dagan et al., 2013; Bowman et al., 2015; Nangia et al., 2017), due to the requirement for models to reason about several difﬁcult linguistic…”
An extended model of natural logic (2009, methodology): “The first is the FraCaS test suite (5), which contains 346 NLI problems, divided into nine sections, each focused on a specific category of semantic phenomena.”
Ordinal Common-sense Inference (2016, methodology): “The data for these tasks are either smaller, carefully constructed evaluation sets by professionals, following efforts like the FRACAS test suite (Cooper et al., 1996), or they rely on crowdsourced elicitation (Bowman et al.”
What Will it Take to Fix Benchmarking in Natural Language Understanding? (2021, methodology): “While this can be helpful when building diagnostic datasets that focus on speciﬁc types of model failure (Cooper et al., 1996; Naik et al., 2018; Wang et al., 2019b), it is counterproductive when our goal is to build a broad-coverage benchmark dataset to set priorities and guide progress toward the…”
Inherent Disagreements in Human Textual Inferences (2019, background): “These takeaways are significant for work in computational semantics and language technology in general primarily because NLI has, historically (Cooper et al., 1996; Dagan et al., 2006) as well as presently (White et al., 2017), been proposed as a means for evaluating a model’s ‘‘intrinsic’’…”
Probing Natural Language Inference Models through Semantic Fragments (2019, methodology): “The use of semantic fragments has a long tradition in logical semantics, starting with the seminal work of Mon-tague (1973), as well as earlier work on NLI (Cooper et al. 1996).”
Automated Fact Checking: Task Formulations, Methods and Future Directions (2018, neutral): “Angeli and Manning (2014) proposed a method of extracting common sense knowledge from WordNet for reasoning about common sense knowledge us-ing Natural Logic and evaluated their approach on a subset of textual entailment problems in the FraCaS test suite (Cooper et al., 1996).”
Textual Entailment with Structured Attentions and Composition (2016, methodology): “…training corpora, which makes it difﬁcult to directly apply neural models on traditional RTE datasets, e.g., the Pascal RTE dataset (Dagan et al., 2006) and the FraCaS dataset (Cooper et al., 1996), which are usually small and contain many named entities that are hard for neural models to identify.”
Grounded Textual Entailment (2018, background): “After formal semanticists developed FraCas in the mid ’90 (Cooper et al., 1996), an increase in statistical approaches to computational semantics gave rise to the need for suitable evaluation datasets.”
Emergent: a novel data-set for stance classification (2016, methodology): “Emergent, being derived from the workflow of journalists is more realistic than data-sets designed for textual entailment such as FraCas (Cooper et al., 1996) and SICK (Marelli et al.”
Combining Natural Logic and Shallow Reasoning for Question Answering (2016, background): “Unlike FraCaS (Cooper et al., 1996) or the RTE challenges, however, the task does not have explicit premises to run inference from, but rather must infer the truth of the hypothesis from a large collection of supporting text.”