Nathalie Friburger

Finite-state transducer cascades to extract named entities in texts

A lot of Named Entity Extraction Systems were created in English thanks to the impulse of MUC conferences. This article describes a Finite-State Transducer Cascade for the extraction of named entities in French journalistic texts. Finite-State Cascades are widely used for Natural Language Processing: a cascade is a series of finite-state transducers applied to a text transforming it. Such transducer cascades allow implementation of syntactic analysis, translation memory and information extraction. We present our general system named CasSys: this system uses the INTEX natural language processing features to realize a transducer cascade. CasSys is not dedicated to the extraction of named entity; we use it for this task but thanks to Intex, it allows syntactic analyses, information extraction or other tasks.

Pattern Mining for Named Entity Recognition

Many evaluation campaigns have shown that knowledge-based and data-driven approaches remain equally competitive for Named Entity Recognition. Our research team has developed CasEN, a symbolic system based on finite state transducers, which achieved promising results during the Ester2 French-speaking evaluation campaign. Despite these encouraging results, manually extending the coverage of such a hand-crafted system is a difficult task. In this paper, we present a novel approach based on pattern mining for NER and to supplement our system’s knowledge base. The system, mXS, exhaustively searches for hierarchical sequential patterns, that aim at detecting Named Entity boundaries. We assess their efficiency by using such patterns in a standalone mode and in combination with our existing system.

Finite-State Transducer Cascade to Extract Proper Names in Texts

This article describes a finite-state cascade for the extraction of person names in texts in French. We extract these proper names in order to categorize and to cluster texts with them. After a finite-state pre-processing (division of the text in sentences, tagging with dictionaries, etc.), a series of finite-state transducers is applied one after the other to the text and locates left and right contexts that indicates the presence of a person name. An evaluation of the results of this extraction is presented.

Extraction of Semantic Relation Between Arabic Named Entities Using Different Kinds of Transducer Cascades

The extraction of Semantic Relationship (SR) is an important task allowing the identification of relevant semantic information in the annotated textual resources. Besides, extracting SR between Named Entities (NE) is a process, which consists in guessing the significant semantic links related to them. This process is very useful to enhance the NLP-application performance, such as Question Answering systems. In this paper, we propose a rule-based method to extract and annotate SR between Arabic NEs (ANE) using an annotated Arabic Wikipedia corpus. In fact, our proposed method is composed of two main cascades regrouping respectively analysis and synthesis transducers. The analysis transducer cascade is dedicated to extract five SR types, which are synonymy, meronymy, accessibility, functional and proximity. However, synthesis one is devoted to normalize the SR and NE annotation using the TEI (Text Encoding Initiative) recommendation. Furthermore, the established transducer cascades are implemented and generated using the CasSys tool available under Unitex linguistic platform. Finally, the obtained results showed by the calculated measure values are encouraging.