Lauren Romeo


2014

pdf bib
Crowdsourcing as a preprocessing for complex semantic annotation tasks
Héctor Martínez Alonso | Lauren Romeo
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This article outlines a methodology that uses crowdsourcing to reduce the workload of experts for complex semantic tasks. We split turker-annotated datasets into a high-agreement block, which is not modified, and a low-agreement block, which is re-annotated by experts. The resulting annotations have higher observed agreement. We identify different biases in the annotation for both turkers and experts.

pdf bib
A cascade approach for complex-type classification
Lauren Romeo | Sara Mendes | Núria Bel
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The work detailed in this paper describes a 2-step cascade approach for the classification of complex-type nominals. We describe an experiment that demonstrates how a cascade approach performs when the task consists in distinguishing nominals from a given complex-type from any other noun in the language. Overall, our classifier successfully identifies very specific and not highly frequent lexical items such as complex-types with high accuracy, and distinguishes them from those instances that are not complex types by using lexico-syntactic patterns indicative of the semantic classes corresponding to each of the individual sense components of the complex type. Although there is still room for improvement with regard to the coverage of the classifiers developed, the cascade approach increases the precision of classification of the complex-type nouns that are covered in the experiment presented.

pdf bib
Choosing which to use? A study of distributional models for nominal lexical semantic classification
Lauren Romeo | Gianluca Lebani | Núria Bel | Alessandro Lenci
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper empirically evaluates the performances of different state-of-the-art distributional models in a nominal lexical semantic classification task. We consider models that exploit various types of distributional features, which thereby provide different representations of nominal behavior in context. The experiments presented in this work demonstrate the advantages and disadvantages of each model considered. This analysis also considers a combined strategy that we found to be capable of leveraging the bottlenecks of each model, especially when large robust data is not available.

pdf bib
Using unmarked contexts in nominal lexical semantic classification
Lauren Romeo | Sara Mendes | Núria Bel
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

pdf bib
Towards the automatic classification of complex-type nominals
Lauren Romeo | Sara Mendes | Núria Bel
Proceedings of the 6th International Conference on Generative Approaches to the Lexicon (GL2013)

pdf bib
Class-based Word Sense Induction for dot-type nominals
Lauren Romeo | Héctor Martínez Alonso | Núria Bel
Proceedings of the 6th International Conference on Generative Approaches to the Lexicon (GL2013)

2012

pdf bib
Using Qualia Information to Identify Lexical Semantic Classes in an Unsupervised Clustering Task
Lauren Romeo | Sara Mendes | Núria Bel
Proceedings of COLING 2012: Posters

pdf bib
Automatic lexical semantic classification of nouns
Núria Bel | Lauren Romeo | Muntsa Padró
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The work we present here addresses cue-based noun classification in English and Spanish. Its main objective is to automatically acquire lexical semantic information by classifying nouns into previously known noun lexical classes. This is achieved by using particular aspects of linguistic contexts as cues that identify a specific lexical class. Here we concentrate on the task of identifying such cues and the theoretical background that allows for an assessment of the complexity of the task. The results show that, despite of the a-priori complexity of the task, cue-based classification is a useful tool in the automatic acquisition of lexical semantic classes.