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Found 33 papers, 8 papers with code
Text Classification in the LLM Era - Where do we stand?
Large Language Models revolutionized NLP and showed dramatic performance improvements across several tasks.
LLMs in Education: Novel Perspectives, Challenges, and Opportunities
This COLING 2025 tutorial is designed for researchers and practitioners interested in the educational applications of NLP and the role LLMs have to play in this area.
Annotation Errors and NER: A Study with OntoNotes 5.0
In terms of entity mentions, we corrected the span and/or type of ~8% of mentions in the dataset, while adding/deleting/splitting/merging a few more.
Dravidian language family through Universal Dependencies lens
The Universal Dependencies (UD) project aims to create a cross-linguistically consistent dependency annotation for multiple languages, to facilitate multilingual NLP.
Scope Ambiguities in Large Language Models
Sentences containing multiple semantic operators with overlapping scope often create ambiguities in interpretation, known as scope ambiguities.
A Multilingual Evaluation of NER Robustness to Adversarial Inputs
Our results showed the NER models we explored across three languages (English, German and Hindi) are not very robust to such changes, as indicated by the fluctuations in the overall F1 score as well as in a more fine-grained evaluation.
Automatic Text Simplification of News Articles in the Context of Public Broadcasting
This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022.
What do we Really Know about State of the Art NER?
Named Entity Recognition (NER) is a well researched NLP task and is widely used in real world NLP scenarios.
A Neural Pairwise Ranking Model for Readability Assessment
Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research.
Trends, Limitations and Open Challenges in Automatic Readability Assessment Research
This article is a brief survey of contemporary research on developing computational models for readability assessment.
Teaching NLP outside Linguistics and Computer Science classrooms: Some challenges and some opportunities
NLP's sphere of influence went much beyond computer science research and the development of software applications in the past decade.
Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling?
Our results indicate that while fine-tuned embeddings are useful for multilingual proficiency modeling, none of the features achieve consistently best performance for all dimensions of language proficiency.
On Understanding the Relation between Expert Annotations of Text Readability and Target Reader Comprehension
To address this gap, we conducted a user study in which over a 100 participants read texts of different reading levels and answered questions created to test three forms of comprehension.
OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification
This paper describes the collection and compilation of the OneStopEnglish corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification.
Experiments with Universal CEFR Classification
The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels.
The Relevance of Text and Speech Features in Automatic Non-native English Accent Identification
This paper describes our experiments with automatically identifying native accents from speech samples of non-native English speakers using low level audio features, and n-gram features from manual transcriptions.
Machine Learning and Applied Linguistics
This entry introduces the topic of machine learning and provides an overview of its relevance for applied linguistics and language learning.
A study of N-gram and Embedding Representations for Native Language Identification
We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU).
Ranked #2 on
Native Language Identification
on italki NLI
Automated assessment of non-native learner essays: Investigating the role of linguistic features
While the results show that the feature set used results in good predictive models with both datasets, the question "what are the most predictive features?"
Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts
Computational approaches to readability assessment are generally built and evaluated using gold standard corpora labeled by publishers or teachers rather than being grounded in observations about human performance.
A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity
Corpora and web texts can become a rich language learning resource if we have a means of assessing whether they are linguistically appropriate for learners at a given proficiency level.
Readability-based Sentence Ranking for Evaluating Text Simplification
We propose a new method for evaluating the readability of simplified sentences through pair-wise ranking.
