Ashjan Alsulaimani


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Improving Diachronic Word Sense Induction with a Nonparametric Bayesian method
Ashjan Alsulaimani | Erwan Moreau
Findings of the Association for Computational Linguistics: ACL 2023

Diachronic Word Sense Induction (DWSI) is the task of inducing the temporal representations of a word meaning from the context, as a set of senses and their prevalence over time. We introduce two new models for DWSI, based on topic modelling techniques: one is based on Hierarchical Dirichlet Processes (HDP), a nonparametric model; the other is based on the Dynamic Embedded Topic Model (DETM), a recent dynamic neural model. We evaluate these models against two state of the art DWSI models, using a time-stamped labelled dataset from the biomedical domain. We demonstrate that the two proposed models perform better than the state of the art. In particular, the HDP-based model drastically outperforms all the other models, including the dynamic neural model.


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An Evaluation Method for Diachronic Word Sense Induction
Ashjan Alsulaimani | Erwan Moreau | Carl Vogel
Findings of the Association for Computational Linguistics: EMNLP 2020

The task of Diachronic Word Sense Induction (DWSI) aims to identify the meaning of words from their context, taking the temporal dimension into account. In this paper we propose an evaluation method based on large-scale time-stamped annotated biomedical data, and a range of evaluation measures suited to the task. The approach is applied to two recent DWSI systems, thus demonstrating its relevance and providing an in-depth analysis of the models.


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CRF-Seq and CRF-DepTree at PARSEME Shared Task 2018: Detecting Verbal MWEs using Sequential and Dependency-Based Approaches
Erwan Moreau | Ashjan Alsulaimani | Alfredo Maldonado | Carl Vogel
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

This paper describes two systems for detecting Verbal Multiword Expressions (VMWEs) which both competed in the closed track at the PARSEME VMWE Shared Task 2018. CRF-DepTree-categs implements an approach based on the dependency tree, intended to exploit the syntactic and semantic relations between tokens; CRF-Seq-nocategs implements a robust sequential method which requires only lemmas and morphosyntactic tags. Both systems ranked in the top half of the ranking, the latter ranking second for token-based evaluation. The code for both systems is published under the GNU General Public License version 3.0 and is available at


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Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking
Alfredo Maldonado | Lifeng Han | Erwan Moreau | Ashjan Alsulaimani | Koel Dutta Chowdhury | Carl Vogel | Qun Liu
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented. The system mainly exploits universal syntactic dependency features through a Conditional Random Fields (CRF) sequence model. The system competed in the Closed Track at the PARSEME VMWE Shared Task 2017, ranking 2nd place in most languages on full VMWE-based evaluation and 1st in three languages on token-based evaluation. In addition, this paper presents an option to re-rank the 10 best CRF-predicted sequences via semantic vectors, boosting its scores above other systems in the competition. We also show that all systems in the competition would struggle to beat a simple lookup baseline system and argue for a more purpose-specific evaluation scheme.