Sense and Reference

By M. Reimer. 2006

805 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7b6f9b6df5eac70b1b5ab3d7ff222e9f18d14c24

How researchers see this paper

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

1. Mohit Bansal
2. Duyu Tang
3. Furu Wei
4. Jianfeng Gao
5. Zhiyuan Liu
6. Zhe Gan
7. Yoshua Bengio
8. Percy Liang

Citations in the corpus

• From Red Wine to Red Tomato: Composition with Context (2017, methodology): “Starting from Plato’s Theaetetus to the early nineteenth century work of Gottlob Frege [20], compositionality is often regarded as a hallmark of intelligence.”
• Neural Sentiment Classification with User and Product Attention (2016, methodology): “According to the principle of compositionality (Frege, 1892), we model the semantic of a document through a hierarchical structure composed of word-level, sentence-level and document-level.”
• Symposium on S. Butterfill and I. Apperly, "How to Construct a Minimal Theory of Mind" (2013, background): “To see why not, consider the following inference: On almost any account of belief, this inference is not valid (Frege 1948, pp. 214-5).”
• Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (2019, methodology): “Based on our hypothesis, loosely inspired by the linguistic “substitution principle” (Frege, 1960), the model will best re-construct the input by discovering and exploiting syntactic regularities of the text.”
• Learning Semantic Representations of Users and Products for Document Level Sentiment Classification (2015, methodology): “We model the semantics of documents based on the principle of compositionality (Frege, 1892), which states that the meaning of a longer expression (e.”
• Effective LSTMs for Target-Dependent Sentiment Classification (2015, methodology): “Afterwards, semantic compositional approaches are used to compute the vector of a sentence/document from the vectors of its constituents based on the principle of compositionality (Frege, 1892).”
• UnNatural Language Inference (2020, background): “ing is taken to be truth-conditional (Frege, 1948; Montague, 1970; Chierchia and McConnell-Ginet, 1990; Heim and Kratzer, 1998).”
• Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (2015, methodology): “Our approach models document semantics based on the principle of compositionality (Frege, 1892), which states that the meaning of a longer expression (e.”
• Sentiment Embeddings with Applications to Sentiment Analysis (2016, background): “tionality [65], which states that the meaning of a longer expression (e.”