CORLEONE - Core Linguistic Entity Online Extraction

This report presents CORLEONE (Core Linguistic Entity Online Extraction) - a pool of loosely coupled general-purpose basic lightweight linguistic processing resources, which can be independently used to identify core linguistic entities and their features in free texts. Currently, CORLEONE consists of five processing resources: (a) a basic tokenizer, (b) a tokenizer which performs fine-grained token classification, (c) a component for performing morphological analysis, and (d) a memory-efficient database-like dictionary look-up component, and (e) sentence splitter. Linguistic resources for several languages are provided. Additionally, CORLEONE includes a comprehensive library of string distance metrics relevant for the task of name variant matching. CORLEONE has been developed in the Java programming language and heavily deploys state-of-the-art finite-state techniques. Noteworthy, CORLEONE components are used as basic linguistic processing resources in ExPRESS, a pattern matching engine based on regular expressions over feature structures and in the real-time news event extraction system, which were developed by the Web Mining and Intelligence Group of the Support to External Security Unit of IPSC. This report constitutes an end-user guide for COLREONE and provides scientifically interesting details of how it was implemented.JRC.G.2-Support to external securit

DFKI finite-state machine toolkit

Finite-state devices such as finite-state automata and finite-state transducers have been known since the emergence of computer science and are recently extensively used in many areas of language technology. The use of finite-state devices is mainly motivated by their time and space efficiency. In this paper we present the Finite-State Machine Toolkit for building, combining and optimizing the finite-state machines, developed at the Language Technology Lab of the German Research Center for Artificial Intelligence

An Intelligent Text Extraction and Navigation System

We present sppc, a high-performance system for intelligent text extraction and navigation from German free text documents. The main purpose of sppc is to extract as much linguistic structure as possible for performing domain-specific processing. sppc consists of a set of domain-independent shallow core components which are realized by means of cascaded weighted finite state machines and generic dynamic tries. All extracted information is represented uniformly in one data structure (called the text chart) in a highly compact and linked form in order to support indexing and navigation through the set of solutions. Germa

Modelling of a Gazetteer Look-up Component

Abstract This paper compares two storage models for gazetteers, nameley the standard one based on numbered indexing automata associated with an auxiliary storage device against a pure finite-state model, the latter being superior in terms of space and time complexity.

The First Cross-Lingual Challenge on Recognition, Normalization and Matching of Named Entities in Slavic Languages

This paper describes the outcomes of the First Multilingual Named Entity Challenge in Slavic Languages. The Challenge targets recognizing mentions of named entities in web documents, their normalization/lemmatization, and cross-lingual matching. The Challenge was organized in the context of the 6th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL-2017 conference. Eleven teams registered for the evaluation, two of which submitted results on schedule, due to the complexity of the tasks and short time available for elaborating a solution. The reported evaluation figures reflect the relatively higher level of complexity of named entity tasks in the context of Slavic languages. Since the Challenge extends beyond the date of the publication of this paper, updates to the results of the participating systems can be found on the official web page of the Challenge.Peer reviewe

An integrated architecture for shallow and deep processing

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis

Corpora and evaluation tools for multilingual named entity grammar development

We present an effort for the development of multilingual named entity grammars in a unification-based finite-state formalism (SProUT). Following an extended version of the MUC7 standard, we have developed Named Entity Recognition grammars for German, Chinese, Japanese, French, Spanish, English, and Czech. The grammars recognize person names, organizations, geographical locations, currency, time and date expressions. Subgrammars and gazetteers are shared as much as possible for the grammars of the different languages. Multilingual corpora from the business domain are used for grammar development and evaluation. The annotation format (named entity and other linguistic information) is described. We present an evaluation tool which provides detailed statistics and diagnostics, allows for partial matching of annotations, and supports user-defined mappings between different annotation and grammar output formats

The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference. Eight teams participated in the competition, which covered four languages and five entity types. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task. Detailed evaluation information is available on the shared task web page.Non peer reviewe

The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference. Eight teams participated in the competition, which covered four languages and five entity types. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task. Detailed evaluation information is available on the shared task web page.Non peer reviewe