Jon Oberlander

Also published as: Jonathan Oberländer


2018

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Up-cycling Data for Natural Language Generation
Amy Isard | Jon Oberlander | Claire Grover
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Homing in on Twitter Users: Evaluating an Enhanced Geoparser for User Profile Locations
Beatrice Alex | Clare Llewellyn | Claire Grover | Jon Oberlander | Richard Tobin
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Twitter-related studies often need to geo-locate Tweets or Twitter users, identifying their real-world geographic locations. As tweet-level geotagging remains rare, most prior work exploited tweet content, timezone and network information to inform geolocation, or else relied on off-the-shelf tools to geolocate users from location information in their user profiles. However, such user location metadata is not consistently structured, causing such tools to fail regularly, especially if a string contains multiple locations, or if locations are very fine-grained. We argue that user profile location (UPL) and tweet location need to be treated as distinct types of information from which differing inferences can be drawn. Here, we apply geoparsing to UPLs, and demonstrate how task performance can be improved by adapting our Edinburgh Geoparser, which was originally developed for processing English text. We present a detailed evaluation method and results, including inter-coder agreement. We demonstrate that the optimised geoparser can effectively extract and geo-reference multiple locations at different levels of granularity with an F1-score of around 0.90. We also illustrate how geoparsed UPLs can be exploited for international information trade studies and country-level sentiment analysis.

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Improving Topic Model Clustering of Newspaper Comments for Summarisation
Clare Llewellyn | Claire Grover | Jon Oberlander
Proceedings of the ACL 2016 Student Research Workshop

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This Table is Different: A WordNet-Based Approach to Identifying References to Document Entities
Shomir Wilson | Alan Black | Jon Oberlander
Proceedings of the 8th Global WordNet Conference (GWC)

Writing intended to inform frequently contains references to document entities (DEs), a mixed class that includes orthographically structured items (e.g., illustrations, sections, lists) and discourse entities (arguments, suggestions, points). Such references are vital to the interpretation of documents, but they often eschew identifiers such as “Figure 1” for inexplicit phrases like “in this figure” or “from these premises”. We examine inexplicit references to DEs, termed DE references, and recast the problem of their automatic detection into the determination of relevant word senses. We then show the feasibility of machine learning for the detection of DE-relevant word senses, using a corpus of human-labeled synsets from WordNet. We test cross-domain performance by gathering lemmas and synsets from three corpora: website privacy policies, Wikipedia articles, and Wikibooks textbooks. Identifying DE references will enable language technologies to use the information encoded by them, permitting the automatic generation of finely-tuned descriptions of DEs and the presentation of richly-structured information to readers.

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Using Term Position Similarity and Language Modeling for Bilingual Document Alignment
Thanh C. Le | Hoa Trong Vu | Jonathan Oberländer | Ondřej Bojar
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2014

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Re-using an Argument Corpus to Aid in the Curation of Social Media Collections
Clare Llewellyn | Claire Grover | Jon Oberlander | Ewan Klein
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This work investigates how automated methods can be used to classify social media text into argumentation types. In particular it is shown how supervised machine learning was used to annotate a Twitter dataset (London Riots) with argumentation classes. An investigation of issues arising from a natural inconsistency within social media data found that machine learning algorithms tend to over fit to the data because Twitter contains a lot of repetition in the form of retweets. It is also noted that when learning argumentation classes we must be aware that the classes will most likely be of very different sizes and this must be kept in mind when analysing the results. Encouraging results were found in adapting a model from one domain of Twitter data (London Riots) to another (OR2012). When adapting a model to another dataset the most useful feature was punctuation. It is probable that the nature of punctuation in Twitter language, the very specific use in links, indicates argumentation class.

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Determiner-Established Deixis to Communicative Artifacts in Pedagogical Text
Shomir Wilson | Jon Oberlander
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Feature-Based Selection of Dependency Paths in Ad Hoc Information Retrieval
K. Tamsin Maxwell | Jon Oberlander | W. Bruce Croft
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Perceptions of Alignment and Personality in Generated Dialogue
Alastair Gill | Carsten Brockmann | Jon Oberlander
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference

2010

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Situated Reference in a Hybrid Human-Robot Interaction System
Manuel Giuliani | Mary Ellen Foster | Amy Isard | Colin Matheson | Jon Oberlander | Alois Knoll
Proceedings of the 6th International Natural Language Generation Conference

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Report on the Second NLG Challenge on Generating Instructions in Virtual Environments (GIVE-2)
Alexander Koller | Kristina Striegnitz | Andrew Gargett | Donna Byron | Justine Cassell | Robert Dale | Johanna Moore | Jon Oberlander
Proceedings of the 6th International Natural Language Generation Conference

2009

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Validating the web-based evaluation of NLG systems
Alexander Koller | Kristina Striegnitz | Donna Byron | Justine Cassell | Robert Dale | Sara Dalzel-Job | Johanna Moore | Jon Oberlander
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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The Software Architecture for the First Challenge on Generating Instructions in Virtual Environments
Alexander Koller | Donna Byron | Justine Cassell | Robert Dale | Johanna Moore | Jon Oberlander | Kristina Striegnitz
Proceedings of the Demonstrations Session at EACL 2009

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Evaluating Centering for Information Ordering Using Corpora
Nikiforos Karamanis | Chris Mellish | Massimo Poesio | Jon Oberlander
Computational Linguistics, Volume 35, Number 1, March 2009

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Report on the First NLG Challenge on Generating Instructions in Virtual Environments (GIVE)
Donna Byron | Alexander Koller | Kristina Striegnitz | Justine Cassell | Robert Dale | Johanna Moore | Jon Oberlander
Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009)

2006

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Whose Thumb Is It Anyway? Classifying Author Personality from Weblog Text
Jon Oberlander | Scott Nowson
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Individuality and Alignment in Generated Dialogues
Amy Isard | Carsten Brockmann | Jon Oberlander
Proceedings of the Fourth International Natural Language Generation Conference

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Data-Driven Generation of Emphatic Facial Displays
Mary Ellen Foster | Jon Oberlander
11th Conference of the European Chapter of the Association for Computational Linguistics

2004

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Evaluating Centering-Based Metrics of Coherence
Nikiforos Karamanis | Massimo Poesio | Chris Mellish | Jon Oberlander
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

2000

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Optimising text quality in generation from relational databases
Michael O’Donnell | Alistair Knott | Jon Oberlander | Chris Mellish
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

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Demonstration of ILEX 3.0
Michael O’Donnell | Alistair Knott | Jon Oberlander | Chris Mellish
INLG’2000 Proceedings of the First International Conference on Natural Language Generation

1998

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Do the Right Thing … but Expect the Unexpected
Jon Oberlander
Computational-Linguistics, Volume 24, Number 3, September 1998

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An Architecture for Opportunistic Text Generation
Chris Mellish | Mick O’Donnell | Jon Oberlander | Alistair Knott
Natural Language Generation

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Experiments Using Stochastic Search for Text Planning
Chris Mellish | Alistair Knott | Jon Oberlander | Mick O’Donnell
Natural Language Generation

1996

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Sources of Flexibility in Dynamic Hypertext Generation
Alistair Knott | Chris Mellish | Jon Oberlander | Mick O’Donnell
Eighth International Natural Language Generation Workshop

1993

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Intentions, Information, and Inference: Two Rhetorical Questions
Jon Oberlander
Intentionality and Structure in Discourse Relations

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Temporal Connectives in a Discourse Context
Alex Lascarides | Jon Oberlander
Sixth Conference of the European Chapter of the Association for Computational Linguistics

1992

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Aspect-Switching and Subordination: the Role of It-Clefts in Discourse
Judy Delin | Jon Oberlander
COLING 1992 Volume 1: The 14th International Conference on Computational Linguistics

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Preventing False Temporal Implicatures: Interactive Defaults for Text Generation
Jon Oberlander | Alex Lascarides
COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics

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Inferring Discourse Relations in Context
Alex Lascarides | Nicholas Asher | Jon Oberlander
30th Annual Meeting of the Association for Computational Linguistics