De Marneffe et al., 2010 - Google Patents
“Was it good? It was provocative.” Learning the meaning of scalar adjectivesDe Marneffe et al., 2010
View PDF- Document ID
- 15698701598108851265
- Author
- De Marneffe M
- Manning C
- Potts C
- Publication year
- Publication venue
- Proceedings of the 48th annual meeting of the association for computational linguistics
External Links
Snippet
Texts and dialogues often express information indirectly. For instance, speakers' answers to yes/no questions do not always straightforwardly convey a 'yes' or 'no'answer. The intended reply is clear in some cases (Was it good? It was great!) but uncertain in others (Was it …
- 230000004044 response 0 abstract description 35
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/30684—Query execution using natural language analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2705—Parsing
- G06F17/271—Syntactic parsing, e.g. based on context-free grammar [CFG], unification grammars
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/30707—Clustering or classification into predefined classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2765—Recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2785—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30731—Creation of semantic tools
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/274—Grammatical analysis; Style critique
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
Similar Documents
Publication | Publication Date | Title |
---|---|---|
De Marneffe et al. | “Was it good? It was provocative.” Learning the meaning of scalar adjectives | |
Addawood et al. | Linguistic cues to deception: Identifying political trolls on social media | |
Tang et al. | A survey on sentiment detection of reviews | |
Shtok et al. | Learning from the past: answering new questions with past answers | |
Resnik | Selection and information: A class-based approach to lexical relationships | |
Rahman et al. | Narrowing the modeling gap: A cluster-ranking approach to coreference resolution | |
Routray et al. | A survey on sentiment analysis | |
Liu et al. | Measuring similarity of academic articles with semantic profile and joint word embedding | |
CN101599071A (en) | The extraction method of conversation text topic | |
Othman et al. | Enhancing question retrieval in community question answering using word embeddings | |
Figueroa | Male or female: What traits characterize questions prompted by each gender in community question answering? | |
Min et al. | Comparative evaluation of lexicons in performing sentiment analysis | |
Spasić et al. | Idiom-based features in sentiment analysis: Cutting the Gordian knot | |
Robertson et al. | The language of dialogue is complex | |
Little et al. | A semantic and syntactic similarity measure for political tweets | |
Michelbacher | Multi-word tokenization for natural language processing | |
Persing et al. | Lightly-supervised modeling of argument persuasiveness | |
Kádár et al. | Learning word meanings from images of natural scenes | |
Zhang et al. | Feature-based assessment of text readability | |
Liu | Toward robust and efficient interpretations of idiomatic expressions in context | |
Liu | Incorporate Out-of-Vocabulary Words for Psycholinguistic Analysis using Social Media Texts-An OOV-Aware Data Curation Process and a Hybrid Approach | |
Gelbukh | Natutal language processing: Perspective of CIC-IPN | |
Matsumoto et al. | Construction of wakamono kotoba emotion dictionary and its application | |
Qin | A framework and practical implementation for sentiment analysis and aspect exploration | |
Farhadloo | Statistical Methods for Aspect Level Sentiment Analysis |