The term “Named Entity”, now widely used in Natural Language Processing, was coined for the Sixth Message Understanding Conference (MUC-6) (R. Grishman & Sundheim 1996). At that time, MUC was focusing on Information Extraction (IE) tasks where structured information of company activities and defense related activities is extracted

In this article, we address the problem of reference disambiguation. Specifically, we consider a situation where entities in the database are referred to using descriptions (e.g., a set of instantiated attributes). The objective of reference disambiguation is to identify the unique entity to which each description corresponds. The key difference between the approach we propose (called RELDC) and the traditional techniques is that RELDC analyzes not only object features but also inter-object relationships to improve the disambiguation quality. Our extensive experiments over two real data sets and over synthetic datasets show that analysis of relationships significantly improves quality of the result.

Named Entity recognition (NER) is an important part of many natural language processing tasks. Most current approaches employ machine learning techniques and require supervised data. However, many languages lack such resources. This paper presents an algorithm to automatically discover Named Entities (NEs) in a resource free language, given a bilingual corpora in which it is weakly temporally aligned with a resource rich language. We observe that NEs have similar time distributions across such corpora, and that they are often transliterated, and develop an algorithm that exploits both iteratively. The algorithm makes use of a new, frequency based, metric for time distributions and a resource free discriminative approach to transliteration. We evaluate the algorithm on an English-Russian corpus, and show high level of NEs discovery in Russian. 1

Named Entity recognition (NER) is an important part of many natural language processing tasks. Current approaches often employ machine learning techniques and require supervised data. However, many languages lack such resources. This paper presents an (almost) unsupervised learning algorithm for automatic discovery of Named Entities (NEs) in a resource free language, given a bilingual corpora in which it is weakly temporally aligned with a resource rich language. NEs have similar time distributions across such corpora, and often some of the tokens in a multi-word NE are transliterated. We develop an algorithm that exploits both observations iteratively. The algorithm makes use of a new, frequency based, metric for time distributions and a resource free discriminative approach to transliteration. Seeded with a small number of transliteration pairs, our algorithm discovers multi-word NEs, and takes advantage of a dictionary (if one exists) to account for translated or partially translated NEs. We evaluate the algorithm on an English-Russian corpus, and show high level of NEs discovery in Russian. 1

The task of identifying synonymous relations and objects, or synonym resolution, is critical for high-quality information extraction. This paper investigates synonym resolution in the context of unsupervised information extraction, where neither hand-tagged training examples nor domain knowledge is available. The paper presents a scalable, fullyimplemented system that runs in O(KN log N) time in the number of extractions, N, and the maximum number of synonyms per word, K. The system, called Resolver, introduces a probabilistic relational model for predicting whether two strings are co-referential based on the similarity of the assertions containing them. On a set of two million assertions extracted from the Web, Resolver resolves objects with 78 % precision and 68 % recall, and resolves relations with 90 % precision and 35 % recall. Several variations of Resolver’s probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic Resolver system allows it to handle polysemous names with 97 % precision and 95 % recall on a data set from the TREC corpus.

Entity matching (a.k.a. record linkage) plays a crucial role in integrating multiple data sources, and numerous matching solutions have been developed. However, the solutions have largely exploited only information available in the mentions and employed a single matching technique. We show how to exploit information about data sources to significantly improve matching accuracy. In particular, we observe that different sources often vary substantially in their level of semantic ambiguity, thus requiring different matching techniques. In addition, it is often beneficial to group and match mentions in related sources first, before considering other sources. These observations lead to a large space of matching strategies, analogous to the space of query evaluation plans considered by a relational optimizer. We propose viewing entity matching as a composition of basic steps into a “match execution plan”. We analyze formal properties of the plan space, and show how to find a good match plan. To do so, we employ ideas from social network analysis to infer the ambiguity and relatedness of data sources. We conducted extensive experiments on several real-world data sets on the Web and in the domain of personal information management (PIM). The results show that our solution significantly outperforms current best matching methods. 1.

rship (Xing et al. 2003), and relative comparisons (Schultz & Joachims 2004). These approaches have shown improvements over traditional similarity functions for different data types such as vectors in Euclidean space, strings, and database records composed of multiple text fields. While these initial results are encouraging, there still remains a large number of similarity functions that are currently unable to adapt to a particular domain. In our research, we attempt to bridge this gap by developing both new learnable similarity functions and methods for their application to particular problems in machine learning and data mining. In preliminary work, we proposed two learnable similarity functions for strings that adapt distance computations given training pairs of equivalent and non-equivalent strings (Bilenko & Mooney 2003a). The first function is based on a probabilistic model of edit distance with affine gaps (Gus- Copyright c # 2004, American Association for Artificial Intelli

Abstract. Nowadays many data mining/analysis applications use the graph analysis techniques for decision making. Many of these techniques are based on the importance of relationships among the interacting units. A number of models and measures that analyze the relationship importance (link structure) have been proposed (e.g., centrality, importance and page rank) and they are generally based on intuition, where the analyst intuitively decides a reasonable model that fits the underlying data. In this paper, we address the problem of learning such models directly from training data. Specifically, we study a way to calibrate a connection strength measure from training data in the context of reference disambiguation problem. Experimental evaluation demonstrates that the proposed model surpasses the best model used for reference disambiguation in the past, leading to better quality of reference disambiguation. 1

Table of contents List of tables........................................................................................................................ iv List of figures....................................................................................................................... v Abstract............................................................................................................................... vi

We describe a generative model for clustering named entities which also models named entity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features from the named entity itself and its syntactic context, and coreference information from an unsupervised pronoun resolver. The model scores 86 % on the MUC-7 named-entity dataset. To our knowledge, this is the best reported score for a fully unsupervised model, and the best score for a generative model. 1