Luciano Del Corro

Also published as: Luciano del Corro


2021

pdf bib
Unsupervised Multi-View Post-OCR Error Correction With Language Models
Harsh Gupta | Luciano Del Corro | Samuel Broscheit | Johannes Hoffart | Eliot Brenner
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We investigate post-OCR correction in a setting where we have access to different OCR views of the same document. The goal of this study is to understand if a pretrained language model (LM) can be used in an unsupervised way to reconcile the different OCR views such that their combination contains fewer errors than each individual view. This approach is motivated by scenarios in which unconstrained text generation for error correction is too risky. We evaluated different pretrained LMs on two datasets and found significant gains in realistic scenarios with up to 15% WER improvement over the best OCR view. We also show the importance of domain adaptation for post-OCR correction on out-of-domain documents.

pdf bib
From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations
Luciano Del Corro | Johannes Hoffart
Proceedings of the Third Workshop on Economics and Natural Language Processing

We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.

2018

pdf bib
A Study of the Importance of External Knowledge in the Named Entity Recognition Task
Dominic Seyler | Tatiana Dembelova | Luciano Del Corro | Johannes Hoffart | Gerhard Weikum
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER). We present a novel modular framework that divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources, such as a knowledge-base, a list of names, or document-specific semantic annotations. Further, we show the effects on performance when incrementally adding deeper knowledge and discuss effectiveness/efficiency trade-offs.

pdf bib
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora
Prabal Agarwal | Jannik Strötgen | Luciano del Corro | Johannes Hoffart | Gerhard Weikum
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval. However, NED quality drops when inputs span long time periods like in archives or historic corpora. This paper presents the first time-aware method for NED that resolves ambiguities even when mention contexts give only few cues. The method is based on computing temporal signatures for entities and comparing these to the temporal contexts of input mentions. Our experiments show superior quality on a newly created diachronic corpus.

pdf bib
Facts That Matter
Marco Ponza | Luciano Del Corro | Gerhard Weikum
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. We also present SalIE, the first fact salience system. SalIE is unsupervised and knowledge agnostic, based on open information extraction to detect facts in natural language text, PageRank to determine their relevance, and clustering to promote diversity. We compare SalIE with several baselines (including positional, standard for saliency tasks), and in an extrinsic evaluation, with state-of-the-art automatic text summarizers. SalIE outperforms baselines and text summarizers showing that facts are an effective way to compress information.

2017

pdf bib
MinIE: Minimizing Facts in Open Information Extraction
Kiril Gashteovski | Rainer Gemulla | Luciano del Corro
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner. In this paper, we propose MinIE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinIE approaches these goals by (1) representing information about polarity, modality, attribution, and quantities with semantic annotations instead of in the actual extraction, and (2) identifying and removing parts that are considered overly specific. We conducted an experimental study with several real-world datasets and found that MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, semantically enriched extractions.

2015

pdf bib
FINET: Context-Aware Fine-Grained Named Entity Typing
Luciano Del Corro | Abdalghani Abujabal | Rainer Gemulla | Gerhard Weikum
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
CORE: Context-Aware Open Relation Extraction with Factorization Machines
Fabio Petroni | Luciano Del Corro | Rainer Gemulla
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

pdf bib
Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning
Luciano Del Corro | Rainer Gemulla | Gerhard Weikum
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)