Search Results for author: David Alfter

Found 16 papers, 2 papers with code

Interconnecting lexical resources and word alignment: How do learners get on with particle verbs?

no code implementations WS (NoDaLiDa) 2019 David Alfter, Johannes Graën

In this paper, we present a prototype for an online exercise aimed at learners of English and Swedish that serves multiple purposes.

Translation Word Alignment

LEGATO: A flexible lexicographic annotation tool

no code implementations WS (NoDaLiDa) 2019 David Alfter, Therese Lindström Tiedemann, Elena Volodina

This article is a report from an ongoing project aiming at analyzing lexical and grammatical competences of Swedish as a Second language (L2).

Lexical Analysis Morphological Analysis

FABRA: French Aggregator-Based Readability Assessment toolkit

no code implementations LREC 2022 Rodrigo Wilkens, David Alfter, Xiaoou Wang, Alice Pintard, Anaïs Tack, Kevin P. Yancey, Thomas François

In this paper, we present the FABRA: readability toolkit based on the aggregation of a large number of readability predictor variables.

A Dictionary-Based Study of Word Sense Difficulty

1 code implementation READI (LREC) 2022 David Alfter, Rémi Cardon, Thomas François

In this article, we present an exploratory study on perceived word sense difficulty by native and non-native speakers of French.

Sentence

Is Attention Explanation? An Introduction to the Debate

no code implementations ACL 2022 Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin

In this paper, we provide a clear overview of the insights on the debate by critically confronting works from these different areas.

Crowdsourcing Relative Rankings of Multi-Word Expressions: Experts versus Non-Experts

no code implementations17 Jun 2022 David Alfter, Therese Lindström Tiedemann, Elena Volodina

In this study we investigate to which degree experts and non-experts agree on questions of difficulty in a crowdsourcing experiment.

An exploratory study of L1-specific non-words

no code implementations2 Sep 2020 David Alfter

In this paper, we explore L1-specific non-words, i. e. non-words in a target language (in this case Swedish) that are re-ranked by a different-language language model.

Language Modelling Re-Ranking

Using Multilingual Resources to Evaluate CEFRLex for Learner Applications

no code implementations LREC 2020 Johannes Gra{\"e}n, David Alfter, Gerold Schneider

The Common European Framework of Reference for Languages (CEFR) defines six levels of learner proficiency, and links them to particular communicative abilities.

Towards Single Word Lexical Complexity Prediction

no code implementations WS 2018 David Alfter, Elena Volodina

In this paper we present work-in-progress where we investigate the usefulness of previously created word lists to the task of single-word lexical complexity analysis and prediction of the complexity level for learners of Swedish as a second language.

General Classification Lexical Complexity Prediction

SB@GU at the Complex Word Identification 2018 Shared Task

no code implementations WS 2018 David Alfter, Ildik{\'o} Pil{\'a}n

In this paper, we describe our experiments for the Shared Task on Complex Word Identification (CWI) 2018 (Yimam et al., 2018), hosted by the 13th Workshop on Innovative Use of NLP for Building Educational Applications (BEA) at NAACL 2018.

Complex Word Identification feature selection +4

Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners' Writings

no code implementations WS 2016 Ildik{\'o} Pil{\'a}n, David Alfter, Elena Volodina

We bring together knowledge from two different types of language learning data, texts learners read and texts they write, to improve linguistic complexity classification in the latter.

Classification Domain Adaptation +1

Analyzer and generator for Pali

1 code implementation6 Oct 2015 David Alfter

This work describes a system that performs morphological analysis and generation of Pali words.

Morphological Analysis

Language Segmentation

no code implementations6 Oct 2015 David Alfter

I compare three approaches: supervised n-gram language models, unsupervised clustering and weakly supervised n-gram language model induction.

Clustering Language Modelling +2

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