Biran, O. Brody, and N. ACL Gasperin, C. Challenging choices for text simplification. Computational Processing of the Portuguese Language. Mahamood, S. ENLG Nenkova, A. Chae, A. Louis and E. O'Neill, R. Should we modify English language for deaf learners?
Petersen, S. Natural language processing tools for reading level assessment and text simplification for bilingual education.
Pitler, E. Louis and A. Power, R. Generating numerical approximations. Computational Linguistics, Volume 38, No. Siddharthan, A. Complex lexico-syntactic reformulation of sentences using typed dependency representations. INLG Woodsend, K. EMNLP Zhu, Z. A monolingual tree-based translation model for sentence simplification. For example, a document isn't relevant to a person's information need - at least, not immediately - if they can't understand it, yet Web search engines have traditionally ignored the problem of finding or providing content at the right level of difficulty as an aspect of relevance.
I'll show how computing and applying metadata based on text readability at Web scale - especially in combination with topic metadata - opens up new and sometimes surprising possibilities for enriching our interactions with the Web, from personalizing Web search results to predicting user and site expertise to estimating searcher motivation.
I'll also discuss future challenges and opportunities in predicting and improving text readability, particularly in light of the rapidly growing interest in large-scale applications for online education.
Toward Determining the Comprehensibility of Machine Translations. Tucker Maney, Linda Sibert, Dennis Perzanowski, Kalyan Gupta and Astrid Schmidt-Nielsen abstract Economic globalization and the needs of the intelligence community have brought machine translation into the forefront. Text quality and summarization are research topics with cross-disciplinary appeal. The PI will offer project-based courses at the undergraduate and graduate level which have the potential to attract young people to the field of computer science.
Some full text articles may not yet be available without a charge during the embargo administrative interval. Some links on this page may take you to non-federal websites. Their policies may differ from this site. Annie Louis and Ani Nenkova. Karolina Owczarzak, John M.
Junyi Jessy Li and Ani Nenkova.
- Reflections on the motive power of heat and on machines fitted to develop this power!
- Contentious Politics in North America: National Protest and Transnational Collaboration under Continental Integration (International Political Economy);
- Conference of the European Chapter of the Association for Computational Linguistics (EACL).
Benjamin Nye and Ani Nenkova. Ariani Di Felippo, Ani Nenkova. Ani Nenkova, Kathleen McKeown. The robotic reporter: Automated journalism and the redefinition of labor, compositional forms, and journalistic authority. Digital Journalism, 3 3 , Graefe, A. Guide to automated journalism. Mapping the field of Algorithmic Journalism.
Empirical Methods in Natural Language [email protected] 2010:
Digital Journalism, 4 6 , Clerwall, C. Enter the robot journalist: Users' perceptions of automated content. Journalism Practice, 8 5 , Van Dalen, A. The algorithms behind the headlines: How machine-written news redefines the core skills of human journalists. Journalism Practice, 6 , You do not have to use all of these, they are just suggestions.
- Read Empirical Methods in Natural Language Generation: Data-oriented Methods and Empirical;
- 14th EACL 2014: Gothenburg, Sweden;
- [email protected]: Generating referring expressions in context : the GREC task evaluation challenges.
- Microbial Ribonucleases!
- Political Communications in Greater China!
- The boardinghouse: the Artist Community House, Chicago 1936-37?
- Cost-Benefit Analysis.
You should certainly also add further literature, for example by looking at references in these articles or searching for relevant papers. Very useful, both for its content and reference list as a source of further literature. Largely template-based, old-fashioned NLG.
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For this topic, do not focus too much on the technical details: we may look more into those in the statistical NLG topic. Recent popular article very high-level about algorithmic journalism. Reading material supplied by the presenters is available here. Below that are the starting points for reading provided by Mark at the beginning of the course.
References from Julian: Article: Choosing words in computer-generated weather forecasts, Reiter et al. References from Myriam: Goldberg, E. Using natural-language processing to produce weather forecasts. IEEE Expert, 9 2 , Stede, M. Lexicalization in natural language generation: A survey. Artificial Intelligence Review, 8 4 , The following also appear under Julian's, above: [ Reiter, E. Choosing words in computer-generated weather forecasts. Artificial Intelligence, , Building natural language generation systems.
Cambridge: Cambridge university press.
Empirical methods in natural language generation : data-oriented methods and empirical evaluation
It uses an automatic weather report generation system as a running example throughout the book. Uses commercial NLG software. Unfortunately, the software itself is no longer available and quite old, anyway , but the manual may give some insight into how the final stages of the traditional pipeline can be implemented in practice. No details of the system at all, but one example of a commercial application of NLG to this task. Article: Choosing words in computer-generated weather forecasts, Reiter et al. Mainly interesting for the concrete system description.
You may wish to leave the more statistical aspects of this for the next session's presenters. Here is reading material submitted by this week's presenters. Below you can find the original suggested starting points for reading. Albert Gatt and Ehud Reiter. Building Natural Language Generation Systems. Ani Nenkova. Automatic Text Summarization of Newswire.
[email protected]: Generating referring expressions in context : the GREC task evaluation challenges
AAAI Press Perhaps look in more detail here at the technical details and use of summarisation techniques. Higher level details probably covered already in Algorithmic Journalism topic. Look particularly at chapters on text-to-text generation and referring expression generation. Many of these focus on subtasks of the classical NLG pipeline that we've seen, typically applying modern machine learning techniques. May have been covered in the previous session, but the statistical approach may be of interest here. Unsupervised concept-to-text generation with hypergraphs. Generative alignment and semantic parsing for learning from ambiguous supervision.
Ioannis Konstas and Mirella Lapata. Concept-to-text generation via discriminative reranking. References from Leo: P.