Named Entity Extraction and Disambiguation: The Reinforcement Effect.

Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u

P-FASTUS: Information Extraction System Implemented in a Constraint Programming Language -SICStus Prolog

P-FASTUS is an Information Extraction system developed in SICStus Prolog based on the implementation of FASTUS. It is program that extracts prespecified information such as the name of the companny, location and the position being advertised from Job PostingIs\u27\u27 in text files. The system is composed of different levels of processing phases that are implemented using finite-state transducers

A classification-based approach to economic event detection in Dutch news text

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text

A Survey of Biological Entity Recognition Approaches

There has been growing interest in the task of Named Entity Recognition (NER) and a lot of research has been done in this direction in last two decades. Particularly, a lot of progress has been made in the biomedical domain with emphasis on identifying domain-specific entities and often the task being known as Biological Named Entity Recognition (BER). The task of biological entity recognition (BER) has been proved to be a challenging task due to several reasons as identified by many researchers. The recognition of biological entities in text and the extraction of relationships between them have paved the way for doing more complex text-mining tasks and building further applications. This paper looks at the challenges perceived by the researchers in BER task and investigates the works done in the domain of BER by using the multiple approaches available for the task

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

Toponym extraction and disambiguation enhancement using loops of feedback

Toponym extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. This paper addresses two problems with toponym extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted named entities without considering the uncertainty and imperfection of the extraction process. In this paper we aim to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust. We conducted experiments with a set of holiday home descriptions with the aim to extract and disambiguate toponyms. We show that the extraction confidence probabilities are useful in enhancing the effectiveness of disambiguation. Reciprocally, retraining the extraction models with information automatically derived from the disambiguation results, improves the extraction models. This mutual reinforcement is shown to even have an effect after several automatic iterations

Improving named entity disambiguation by iteratively enhancing certainty of extraction

Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. This paper addresses two problems with named entity extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted named entities without considering the uncertainty and imperfection of the extraction process. It is the aim of this paper to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust. We conducted experiments with a set of holiday home descriptions with the aim to extract and disambiguate toponyms as a representative example of named entities. We show that the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how retraining the extraction models with information automatically derived from the disambiguation results, improves the extraction models. This mutual reinforcement is shown to even have an effect after several iterations

The multilingual entity task (MET) overview

Conference-6 (MUC-6) evaluation of named entity identification demonstrated that systems are approach-ing human performance onEnglish language t xts [10]. Informal and anonymous, the MET provided a new opportunity to assess progress on the same task in Span-ish, Japanese, and Chinese. Preliminary results indicate that MET systems in all three languages performed comparably to those of the MUC-6 evaluatien in English. Based upon the Named Entity Task Guidelines [ 11], the task was to locate and tag with SGML named entity expressions (people, organizations, and locations), time expressions (time and date), and numeric expressions (percentage and money) in Spanish texts from Agence France Presse, in Japanese texts from Kyodo newswire, or in Chinese texts from Xinhua newswkel. Across lan-guages the keywords "press conference " retrieved a rich subcorpus of texts, covering awide spectrum of topics. Frequency and types of expressions vary in the three language sets [2] [8] [9]. The original task guidelines were modified so that he core guidelines were language independent with language specific rules appended. The schedule was quite abbreviated. In the fall, Government language teams retrieved training and test texts with multilingual software for the Fast Data Finder (FDF), refined the MUC-6 guidelines, and manually tagged 100 training texts using the SRA Named Entity Tool. In January, the training texts were released along with 200 sample unannotated training texts to the partic-ipating sites. A dry run was held in late March and early April and in late April the official test on 100 texts was. The language t xts were supplied by the Linguistic Data Consortium (LDC) at the University of Pennsylvania. performed anonymously. SAIC created language ver-sions of the scoring program and provided technical support throughout. Both commercial and academic groups partici-pated. Two groups, New Mexico State University/Com