Generating Lexical Representations of Frames using Lexical Substitution

no code implementations • PaM 2020 • Saba Anwar, Artem Shelmanov, Alexander Panchenko, Chris Biemann

We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs.

More Like This: Semantic Retrieval with Linguistic Information

no code implementations • KONVENS (WS) 2022 • Steffen Remus, Gregor Wiedemann, Saba Anwar, Fynn Petersen-Frey, Seid Muhie Yimam, Chris Biemann

Retrieval Semantic Retrieval

SCoT: Sense Clustering over Time: a tool for the analysis of lexical change

no code implementations • EACL 2021 • Christian Haase, Saba Anwar, Seid Muhie Yimam, Alexander Friedrich, Chris Biemann

There are two main approaches to the exploration of dynamic networks: the discrete one compares a series of clustered graphs from separate points in time.

Clustering

HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings

1 code implementation • SEMEVAL 2019 • Saba Anwar, Dmitry Ustalov, Nikolay Arefyev, Simone Paolo Ponzetto, Chris Biemann, Alexander Panchenko

We present our system for semantic frame induction that showed the best performance in Subtask B. 1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019).

Clustering Task 2 +1