Conceptual dependency: A theory of natural language understanding
By Roger C. Schank. 1972
Subfields: Natural Language Processing
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
- Soujanya Poria
- Erik Cambria
- Dan Jurafsky
- Eduard H. Hovy
- Devamanyu Hazarika
- Percy Liang
- Mirella Lapata
- Josh Tenenbaum
Citations in the corpus
- What can the brain teach us about building artificial intelligence? (2016, background): “xplicitly referenced human cognition, and even published papers in cognitive psychology journals (e.g., 6 Bobrow & Winograd, 1977; Hayes-Roth & Hayes-Roth, 1979; Winograd, 1972). For example, Schank (1972), writing in the journal Cognitive Psychology, declared that We hope to be able to buil”
- Building Machines that Learn and Think Like People (2018, background): “For example, Schank (1972), writing in the journal Cognitive Psychology, declared that”
- Complex Declarative Learning. (2005, background): “…answer is that the immune system continually generates more or less random antibodies; high fit between antibodies and antigens triggers increased production of the former; thus, the antigens themselves function as an environment that selects for the antibodies that fight them (Gazzinga, 1992).”
- Automatic Labeling of Semantic Roles (2000, background): “There are scores of other theories with slightly di erent sets of roles, including, among many others, (Fillmore, 1968), (Jackendo , 1972), (Schank, 1972); see (Somers, 1987) for an excellent summary.”
- SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (2016, methodology): “This generalization is inspired by different theories on conceptual primitives, including Roger Schank’s conceptual dependency theory (Schank, 1972), Ray Jackendoff’s work on explanatory semantic representation (Jackendoff, 1976), and Anna Wierzbicka’s book on primes and universals (Wierzbicka,…”
- Natural language processing (NLP) in management research: A literature review (2020, background): “The principal mission of NLU is to comprehend the natural language (human language) (Schank, 1972) by deciphering documents and extracting valuable information for downstream tasks.”
- Open-domain textual question answering techniques (2003, methodology): “In Qualm , however, all these phases are dependent upon the Conceptual Dependency schema that has several limitations, the major one being the inability to operate on arbitrary documents in various domains.”
- NAREOR: The Narrative Reordering Problem (2021, background): “It has been well-established that readers remember only an abstract representation of stories (Schank 1972).”
- Goal-Based Explanation Evaluation (1991, unknown): “ACCEPTER takes as input a story represented in terms of Conceptual Dependency theory primitives (Schank, 1972), or in terms of schemas packaging sequences of those primitives to represent stereotyped sequences of actions.”
- The misinterpretation of noncanonical sentences. (2003, unknown): “It is important to note, however, that this model has had its proponents, particularly among those who focus on the comprehension of multi-sentence discourses (Lehnert & Ringle, 1982; Osgood, 1980; Schank, 1972 ).”
- SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings (2018, background): “This generalization is inspired by different theories on conceptual primitives (Schank 1972; Jackendoff 1976; Wierzbicka 1996), but also theoretical studies on knowledge representation (Minsky 1975; Rumelhart and Ortony 1977).”
- Formal Distributional Semantics: Introduction to the Special Issue (2016, background): “At the same time, Cognitive Science and Artificial Intelligence were theorizing in which ways natural language understanding—seen as a broad, all-encompassing task—might be modeled and tested (Schank 1972; Winograd 1972).”
- Case-Based Reasoning: A Research Paradigm (1991, unknown): “Schank (1972, 1975a) and his students developed natural language systems for representing concepts and understanding single sentences.”