Empirical Methods in Natural Language Generation Data-oriented Methods and Empirical Evaluation /

Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. In recent years the field ha...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Krahmer, Emiel (Επιμελητής έκδοσης), Theune, Mariët (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
Σειρά:Lecture Notes in Computer Science, 5790
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Text-to-Text Generation
  • Probabilistic Approaches for Modeling Text Structure and Their Application to Text-to-Text Generation
  • Spanning Tree Approaches for Statistical Sentence Generation
  • On the Limits of Sentence Compression by Deletion
  • NLG in Interaction
  • Learning Adaptive Referring Expression Generation Policies for Spoken Dialogue Systems
  • Modelling and Evaluation of Lexical and Syntactic Alignment with a Priming-Based Microplanner
  • Natural Language Generation as Planning under Uncertainty for Spoken Dialogue Systems
  • Referring Expression Generation
  • Generating Approximate Geographic Descriptions
  • A Flexible Approach to Class-Based Ordering of Prenominal Modifiers
  • Attribute-Centric Referring Expression Generation
  • Evaluation of NLG
  • Assessing the Trade-Off between System Building Cost and Output Quality in Data-to-Text Generation
  • Human Evaluation of a German Surface Realisation Ranker
  • Structural Features for Predicting the Linguistic Quality of Text
  • Towards Empirical Evaluation of Affective Tactical NLG
  • Shared Task Challenges for NLG
  • Introducing Shared Tasks to NLG: The TUNA Shared Task Evaluation Challenges
  • Generating Referring Expressions in Context: The GREC Task Evaluation Challenges
  • The First Challenge on Generating Instructions in Virtual Environments.