activity
20162022
most citedBenchmark for Complex Answer Retrieval

9 citations · 14 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL20222 cited

Identifying Moments of Change from Longitudinal User Text

Adam Tsakalidis, Federico Nanni, Anthony Hills +3

Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic…

cs.CL20203 cited

A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching

Mariona Coll Ardanuy, Kasra Hosseini, Katherine McDonough +3

Recognizing toponyms and resolving them to their real-world referents is required for providing advanced semantic access to textual data. This process is often hindered by the high…

cs.CL2020

Living Machines: A study of atypical animacy

Mariona Coll Ardanuy, Federico Nanni, Kaspar Beelen +7

This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypi…

cs.CL2019

Event-based Access to Historical Italian War Memoirs

Marco Rovera, Federico Nanni, Simone Paolo Ponzetto

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among th…

cs.IR20179 cited

Benchmark for Complex Answer Retrieval

Federico Nanni, Bhaskar Mitra, Matt Magnusson +1

Retrieving paragraphs to populate a Wikipedia article is a challenging task. The new TREC Complex Answer Retrieval (TREC CAR) track introduces a comprehensive dataset that targets…

cs.CL2016

Entities as topic labels: Improving topic interpretability and evaluability combining Entity Linking and Labeled LDA

Federico Nanni, Pablo Ruiz Fabo

In order to create a corpus exploration method providing topics that are easier to interpret than standard LDA topic models, here we propose combining two techniques called Entity…