Hamidreza Saghir


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Robust Candidate Generation for Entity Linking on Short Social Media Texts
Liam Hebert | Raheleh Makki | Shubhanshu Mishra | Hamidreza Saghir | Anusha Kamath | Yuval Merhav
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)

Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity, among other issues. We investigate these challenges on a large and recent Tweets benchmark for EL, empirically evaluate lookup and dense retrieval approaches, and demonstrate a hybrid solution using long contextual representation from Wikipedia is necessary to achieve considerable gains over previous work, achieving 0.93 recall.


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A Cross-Domain Transferable Neural Coherence Model
Peng Xu | Hamidreza Saghir | Jin Sung Kang | Teng Long | Avishek Joey Bose | Yanshuai Cao | Jackie Chi Kit Cheung
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.