Named Entity metonymy resolution is a challenging natural langage processing task, which has been recently subject to a growing interest. In this paper, we describe the method we have developed in order to solve Named entity metonymy in the framework of the SemEval 2007 competition. In order to perform Named Entity metonymy resolution on location names and company names, as required for this ta...

Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that su...

This work studies Named Entity Recognition (NER) for Catalan without making use of annotated resources of this language. The approach presented is based on machine learning techniques and exploits Spanish resources, either by first training models for Spanish and then translating them into Catalan, or by directly training bilingual models. The resulting models are retrained on unlabelled Catala...

This paper presents the 2005 MIRACLE team’s approach to CrossLanguage Geographical Retrieval (GeoCLEF). The main goal of the GeoCLEF participation of the MIRACLE team was to test the effect that geographical information retrieval techniques have on information retrieval. The baseline approach is based on the development of named entity recognition and geospatial information retrieval tools and ...

Some languages lack large knowledge bases and good discriminative features for Name Entity Recognition (NER) that can generalize to previously unseen named entities. One such language is Arabic, which: a) lacks a capitalization feature; and b) has relatively small knowledge bases, such as Wikipedia. In this work we address both problems by incorporating cross-lingual features and knowledge base...

Named Entity Recognition (NER) systems need to integrate a wide variety of information for optimal performance. This paper demonstrates that a maximum entropy tagger can effectively encode such information and identify named entities with very high accuracy. The tagger uses features which can be obtained for a variety of languages and works effectively not only for English, but also for other l...

This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5% and 1...

Named entity recognition often fails in idiosyncratic domains. That causes a problem for depending tasks, such as entity linking and relation extraction. We propose a generic and robust approach for high-recall named entity recognition. Our approach is easy to train and offers strong generalization over diverse domainspecific language, such as news documents (e.g. Reuters) or biomedical text (e...

In this paper, we present Thai named entity recognition (NER) systems using supervised Conditional Random Fields (CRFs) with various answer patterns to find out whether different answer patterns would affect the performance of the systems. Every system used the same set of features except the answers in the training corpus. There are 5 patterns of answer used in this study. The results show tha...