Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior methods for relation discovery, however, needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for relation discovery from large corpora. The key idea is clustering pair...

A number of research and software development groups have developed name identification technology, but few have addressed the issue of cross-document coreference, or identifying the same named entities across documents. In a collection of documents, where there are multiple discourse contexts, there exists a manyto-many correspondence between names and entities, making it a challenge to automa...

Named entity recognition (NER) systems are important for extracting useful information from unstructured data sources. It is known that large domain dictionaries help in improving extraction performance of NER. Unstructured text usually contains entity mentions that are different from their standard dictionary form. Approximate matching is important to identify the correct dictionary entity for...

The European Commission’s (EC) Directorate General for Translation, together with the EC’s Joint Research Centre, is making available a large translation memory (TM; i.e. sentences and their professionally produced translations) covering twenty-two official European Union (EU) languages and their 231 language pairs. Such a resource is typically used by translation professionals in combination w...

Although community discovery based on social network has been studied extensively in the Web hyperlink environment, limited research has been done in the case of Web documents. The co-occurrence of Words and entities in sentences and documents usually implies some connections among them. Studying such connections may reveal important relationships. In this paper, we investigate the cooccurrence...

To aim at the evaluation task of CLP2012 named entity recognition and disambiguation in Chinese, a Chinese name disambiguation method based on adaptive clustering with the attribute features is proposed. Firstly, 12-dimensional character attribute features is defined, and tagged attribute feature corpus are used to train to obtain the recognition model of attribute features by Conditional Rando...

The ambiguity of person names in the Web has become a new area of interest for NLP researchers. This challenging problem has been formulated as the task of clustering Web search results (returned in response to a person name query) according to the individual they mention. In this paper we compare the coverage, reliability and independence of a number of features that are potential information ...

In this paper, we apply the concept of pretraining to hidden-unit conditional random fields (HUCRFs) to enable learning on unlabeled data. We present a simple yet effective pre-training technique that learns to associate words with their clusters, which are obtained in an unsupervised manner. The learned parameters are then used to initialize the supervised learning process. We also propose a w...

Short context messages (like tweets and SMS’s) are a potentially rich source of continuously and instantly updated information. Shortness and informality of such messages are challenges for Natural Language Processing tasks. Most efforts done in this direction rely on machine learning techniques which are expensive in terms of data collection and training. In this paper we present an unsupervis...

In this paper, we propose a new method for semantic class induction. First, we introduce a generative model of sentences, based on dependency trees and which takes into account homonymy. Our model can thus be seen as a generalization of Brown clustering. Second, we describe an efficient algorithm to perform inference and learning in this model. Third, we apply our proposed method on two large d...