47 citations · 131 across the 6 of their papers we have counts for
6 papers
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization
Mohamed Trabelsi, Huseyin Uzunalioglu
Multi-document summarization (MDS) refers to the task of summarizing the text in multiple documents into a concise summary. The generated summary can save the time of reading many…
DAME: Domain Adaptation for Matching Entities
Mohamed Trabelsi, Jeff Heflin, Jin Cao
Entity matching (EM) identifies data records that refer to the same real-world entity. Despite the effort in the past years to improve the performance in EM, the existing methods s…
StruBERT: Structure-aware BERT for Table Search and Matching
Mohamed Trabelsi, Zhiyu Chen, Shuo Zhang +2
A large amount of information is stored in data tables. Users can search for data tables using a keyword-based query. A table is composed primarily of data values that are organize…
Neural ranking models for document retrieval
Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison +1
Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-craf…
Semantic Labeling Using a Deep Contextualized Language Model
Mohamed Trabelsi, Jin Cao, Jeff Heflin
Generating schema labels automatically for column values of data tables has many data science applications such as schema matching, and data discovery and linking. For example, aut…
Table Search Using a Deep Contextualized Language Model
Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin +2
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining task…