In this article, we present a new clustering algorithm for Person Name Disambiguation in web search results. The algorithm groups web results according to the individuals they refer to. The best state‐of‐the‐art approaches require training data in order to learn thresholds for deciding when to group the webpages. However, the ambiguity level of person names on the web could not be previously estimated and the results of those methods strongly depend on the thresholds obtained with the training collections. We present the concept of adaptive threshold, which avoids the need of a previous supervised learning process, depending only on the content of the compared documents to decide if they refer to the same person. We evaluated our approach using three datasets reaching close results to those obtained by the most successful algorithms in the state‐of‐the‐art that require such a learning process, and outperforming the results of those obtained by algorithms that do not require it.

In this article, we present a new algorithm for clustering a bilingual collection of comparable news items in groups of specific topics. Our hypothesis is that named entities (NEs) are more informative than other features in the news when clustering fine grained topics. The algorithm does not need as input any information related to the number of clusters, and carries out the clustering only based on information regarding the shared named entities of the news items. This proposal is evaluated using different data sets and outperforms other state‐of‐the‐art algorithms, thereby proving the plausibility of the approach. In addition, because the applicability of our approach depends on the possibility of identifying equivalent named entities among the news, we propose a heuristic system to identify equivalent named entities in the same and different languages, thereby obtaining good performance.

Cognates are words in different languages that have similar spelling and meaning. The identification of cognates is very useful for many different Natural Language Processing tasks, and also in the process of learning a second language. This paper presents a new approach to classify pairs of words into cognates/false friends or not related classes. The proposed approach uses a fuzzy system to combine complementary string similarity measures in order to improve the cognate identification task. The underlying hypothesis is that the combination of different string measures by applying heuristic knowledge, can outperform those measures working separately. The results obtained by the proposed system confirm the previous hypothesis, and furthermore it also outperforms other systems that combine string measures by using a supervised approach. As an additional contribution, we have created a bilingual test data set which include pairs of cognates, false friends and unrelated words in Spanish and English, that is freely available for research purposes.

This paper presents an approach for Multilingual Document Clustering in comparable corpora. The algorithm is of heuristic nature and it uses as unique evidence for clustering the identification of cognate named entities between both sides of the comparable corpora. One of the main advantages of this approach is that it does not depend on bilingual or multilingual resources. However, it depends on the possibility of identifying cognate named entities between the languages used in the corpus. An additional advantage of the approach is that it does not need any information about the right number of clusters; the algorithm calculates it. We have tested this approach with a comparable corpus of news written in English and Spanish. In addition, we have compared the results with a system which translates selected document features. The obtained results are encouraging.