Share This Author
Cross-topic Argument Mining from Heterogeneous Sources
- Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, Iryna Gurevych
- Computer ScienceEMNLP
- 4 November 2018
This paper proposes a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts and shows that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs in F1 in two- and three-label cross-topic settings.
Efficient defeasible reasoning systems
- Michael J. Maher, Andrew Rock, G. Antoniou, D. Billington, Tristan Miller
- Computer ScienceProceedings 12th IEEE Internationals Conference…
- 13 November 2000
It is believed that defeasible logic, with its efficiency and simplicity is a good candidate to be used as a modelling language for practical applications, including modelling of regulations and business rules.
Essay Assessment with Latent Semantic Analysis
- Tristan Miller
- Computer Science
- 1 December 2003
This article examines the application of LSA to automated essay scoring, and compares LSA methods to earlier statistical methods for assessing essay quality, and critically review contemporary essay-scoring systems built on LSA.
SemEval-2017 Task 7: Detection and Interpretation of English Puns
The first competitive evaluation for the automatic detection, location, and interpretation of puns is described, which describes the motivation for these tasks, the evaluation methods, and the manually annotated data set.
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
A new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts that generalizes best to unseen topics and outperforms vanilla BiLSTM models by 6% in accuracy and 11% in F-score is proposed.
ArgumenText: Searching for Arguments in Heterogeneous Sources
This paper presents an argument retrieval system capable of retrieving sentential arguments for any given controversial topic, and finds that its system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.
Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation
A distributional thesaurus is used, computed from a large parsed corpus, for lexical expansion of context and sense information and it is shown that distributional information significantly improves disambiguation results across several data sets.
Automatic disambiguation of English puns
This paper describes how traditional, language-agnostic WSD approaches can be adapted to "disambiguate" puns, or rather to identify their double meanings and evaluates several such approaches on a manually sense-annotated corpus of English puns.
Towards the automatic detection and identification of English puns
A case for research into computational methods for the detection of puns in running text and for the isolation of the intended meanings is made and plans for evaluating WSD-inspired systems in a dedicated pun identification task are outlined.
DKPro WSD: A Generalized UIMA-based Framework for Word Sense Disambiguation
- Tristan Miller, Nicolai Erbs, Hans-Peter Zorn, Torsten Zesch, Iryna Gurevych
- Computer ScienceACL
- 1 August 2013
Implementations of word sense disambiguation (WSD) algorithms tend to be tied to a particular test corpus format and sense inventory. This makes it difficult to test their performance on new data…