Use of Wikipedia Categories in Entity Ranking

Wikipedia is a useful source of knowledge that has many applications in language processing and knowledge representation. The Wikipedia category graph can be compared with the class hierarchy in an ontology; it has some characteristics in common as well as some differences. In this paper, we present our approach for answering entity ranking queries from the Wikipedia. In particular, we explore how to make use of Wikipedia categories to improve entity ranking effectiveness. Our experiments show that using categories of example entities works significantly better than using loosely defined target categories

Media monitoring and information extraction for the highly inflected agglutinative language Hungarian

The Europe Media Monitor (EMM) is a fully-automatic system that analyses written online news by gathering articles in over 70 languages and by applying text analysis software for currently 21 languages, without using linguistic tools such as parsers, part-of-speech taggers or morphological analysers. In this paper, we describe the effort of adding to EMM Hungarian text mining tools for news gathering; document categorisation; named entity recognition and classification for persons, organisations and locations; name lemmatisation; quotation recognition; and cross-lingual linking of related news clusters. The major challenge of dealing with the Hungarian language is its high degree of inflection and agglutination. We present several experiments where we apply linguistically light-weight methods to deal with inflection and we propose a method to overcome the challenges. We also present detailed frequency lists of Hungarian person and location name suffixes, as found in real-life news texts. This empirical data can be used to draw further conclusions and to improve existing Named Entity Recognition software. Within EMM, the solutions described here will also be applied to other morphologically complex languages such as those of the Slavic language family. The media monitoring and analysis system EMM is freely accessible online via the web pag

Arabic named entity recognition

En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimiento que a~nade al texto. En la literatura existen diversos trabajos que investigan la tarea de REN para un idioma especifico o desde una perspectiva independiente del lenguaje. Sin embargo, hasta el momento, se han publicado muy pocos trabajos que estudien dicha tarea para el arabe. El arabe tiene una ortografia especial y una morfologia compleja, estos aspectos aportan nuevos desafios para la investigacion en la tarea de REN. Una investigacion completa del REN para elarabe no solo aportaria las tecnicas necesarias para conseguir un alto rendimiento, sino que tambien proporcionara un analisis de los errores y una discusion sobre los resultados que benefician a la comunidad de investigadores del REN. El objetivo principal de esta tesis es satisfacer esa necesidad. Para ello hemos: 1. Elaborado un estudio de los diferentes aspectos del arabe relacionados con dicha tarea; 2. Analizado el estado del arte del REN; 3. Llevado a cabo una comparativa de los resultados obtenidos por diferentes tecnicas de aprendizaje automatico; 4. Desarrollado un metodo basado en la combinacion de diferentes clasificadores, donde cada clasificador trata con una sola clase de entidades nombradas y emplea el conjunto de caracteristicas y la tecnica de aprendizaje automatico mas adecuados para la clase de entidades nombradas en cuestion. Nuestros experimentos han sido evaluados sobre nueve conjuntos de test.Benajiba, Y. (2009). Arabic named entity recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8318Palanci

Developing a Hybrid Dictionary-based Bio-entity Recognition Technique

Background: Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods: This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results: The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions: The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.X1133Ysciescopu

Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

Named Entity Recognition and Classification (NERC) is an important component of applications like Opinion Tracking, Information Extraction, or Question Answering. When these applications require to work in several languages, NERC becomes a bottleneck because its development requires language-specific tools and resources like lists of names or annotated corpora. This paper presents a lightly supervised system that acquires lists of names and linguistic patterns from large raw text collections in western languages and starting with only a few seeds per class selected by a human expert. Experiments have been carried out with English and Spanish news collections and with the Spanish Wikipedia. Evaluation of NE classification on standard datasets shows that NE lists achieve high precision and reveals that contextual patterns increase recall significantly. Therefore, it would be helpful for applications where annotated NERC data are not available such as those that have to deal with several western languages or information from different domains.This researchwork has been supported by the Regional Government of Madrid under the Research Network MA2VICMR (S2009/TIC-1542), by the Spanish Ministry of Education under the project MULTIMEDICA (TIN2010-20644-C03-01) and by the Spanish Center for Industry Technological Development (CDTI, Ministry of Industry, Tourism and Trade), through the BUSCAMEDIA Project (CEN-20091026)

Named Entity Recognition for the Estonian Language

Käesoleva töö raames uuriti eestikeelsetes tekstides nimega üksuste tuvastamise probleemi (NÜT) kasutades masinõppemeetodeid. NÜT süsteemi väljatöötamisel käsitleti kahte põhiaspekti: nimede tuvastamise algoritmi valikut ja nimede esitusviisi. Selleks võrreldi maksimaalse entroopia (MaxEnt) ja lineaarse ahela tinglike juhuslike väljade (CRF) masinõppemeetodeid. Uuriti, kuidas mõjutavad masinõppe tulemusi kolme liiki tunnused: 1) lokaalsed tunnused (sõnast saadud informatsioon), 2) globaalsed tunnused (sõna kõikide esinemiskontekstide tunnused) ja 3) väline teadmus (veebist saadud nimede nimekirjad). Masinõppe algoritmide treenimiseks ja võrdlemiseks annoteeriti käsitsi ajakirjanduse artiklitest koosnev tekstikorpus, milles märgendati asukohtade, inimeste, organisatsioonide ja ehitise-laadsete objektide nimed. Eksperimentide tulemusena ilmnes, et CRF ületab oluliselt MaxEnt meetodit kõikide vaadeldud nimeliikide tuvastamisel. Parim tulemus, 0.86 F1 skoor, saavutati annoteeritud korpusel CRF meetodiga, kasutades kombinatsiooni kõigist kolmest nime esitusvariandist. Vaadeldi ka süsteemi kohanemisvõimet teiste tekstižanridega spordi domeeni näitel ja uuriti võimalusi süsteemi kasutamiseks teistes keeltes nimede tuvastamisel.In this thesis we study the applicability of recent statistical methods to extraction of named entities from Estonian texts. In particular, we explore two fundamental design challenges: choice of inference algorithm and text representation. We compare two state-of-the-art supervised learning methods, Linear Chain Conditional Random Fields (CRF) and Maximum Entropy Model (MaxEnt). In representing named entities, we consider three sources of information: 1) local features, which are based on the word itself, 2) global features extracted from other occurrences of the same word in the whole document and 3) external knowledge represented by lists of entities extracted from the Web. To train and evaluate our NER systems, we assembled a text corpus of Estonian newspaper articles in which we manually annotated names of locations, persons, organisations and facilities. In the process of comparing several solutions we achieved F1 score of 0.86 by the CRF system using combination of local and global features and external knowledge