Developing an innovative entity extraction method for unstructured data

The main goal of this study is to build high-precision extractors for entities such as Person and Organization as a good initial seed that can be used for training and learning in machine-learning systems, for the same categories, other categories, and across domains, languages, and applications. The improvement of entities extraction precision also increases the relationships extraction precision, which is particularly important in certain domains (such as intelligence systems, social networking, genetic studies, healthcare, etc.). These increases in precision improve the end users’ experience quality in using the extraction system because it lowers the time that users spend for training the system and correcting outputs, focusing more on analyzing the information extracted to make better data-driven decisions

Unsupervised entity linking using graph-based semantic similarity

Nowadays, the human textual data constitutes a great proportion of the shared information resources such as World Wide Web (WWW). Social networks, news and learning resources as well as Knowledge Bases (KBs) are just the small examples that widely contain the textual data which is used by both human and machine readers. The nature of human languages is highly ambiguous, means that a short portion of a textual context (such as words or phrases) can semantically be interpreted in different ways. A language processor should detect the best interpretation depending on the context in which each word or phrase appears. In case of human readers, the brain is quite proficient in interfering textual data. Human language developed in a way that reflects the innate ability provided by the brain’s neural networks. However, there still exist the moments that the text disambiguation task would remain a hard challenge for the human readers. In case of machine readers, it has been a long-term challenge to develop the ability to do natural language processing and machine learning. Different interpretation can change the broad range of topics and targets. The different in interpretation can cause serious impacts when it is used in critical domains that need high precision. Thus, the correctly inferring the ambiguous words would be highly crucial. To tackle it, two tasks have been developed: Word Sense Disambiguation (WSD) to infer the sense (i.e. meaning) of ambiguous words, when the word has multiple meanings, and Entity Linking (EL) (also called, Named Entity Disambiguation–NED, Named Entity Recognition and Disambiguation–NERD, or Named Entity Normalization–NEN) which is used to explore the correct reference of Named Entity (NE) mentions occurring in documents. The solution to these problems impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference. This document summarizes the works towards developing an unsupervised Entity Linking (EL) system using graph-based semantic similarity aiming to disambiguate Named Entity (NE) mentions occurring in a target document. The EL task is highly challenging since each entity can usually be referred to by several NE mentions (synonymy). In addition, a NE mention may be used to indicate distinct entities (polysemy). Thus, much effort is necessary to tackle these challenges. Our EL system disambiguates the NE mentions in several steps. For each step, we have proposed, implemented, and evaluated several approaches. We evaluated our EL system in TAC-KBP4 English EL evaluation framework in which the system input consists of a set of queries, each containing a query name (target NE mention) along with start and end offsets of that mention in the target document. The output is either a NE entry id in a reference Knowledge Base (KB) or a Not-in-KB (NIL) id in the case that system could not find any appropriate entry for that query. At the end, we have analyzed our result in different aspects. To disambiguate query name we apply a graph-based semantic similarity approach to extract the network of the semantic knowledge existing in the content of target document.Este documento es un resumen del trabajo realizado para la construccion de un sistema de Entity Linking (EL) destinado a desambiguar menciones de Entidades Nombradas (Named Entities, NE) que aparecen en un documento de referencia. La tarea de EL presenta una gran dificultad ya que cada entidad puede ser mencionada de varias maneras (sinonimia). Ademas cada mencion puede referirse a mas de una entidad (polisemia). Asi pues, se debe realizar un gran esfuerzo para hacer frente a estos retos. Nuestro sistema de EL lleva a cabo la desambiguacion de las menciones de NE en varias etapas. Para cada etapa hemos propuesto, implementado y evaluado varias aproximaciones. Hemos evaluado nuestro sistema de EL en el marco del TAC-KBP English EL evaluation framework. En este marco la evaluacion se realiza a partir de una entrada que consiste en un conjunto de consultas cada una de las cuales consta de un nombre (query name) que corresponde a una mencion objetivo cuya posicion en un documento de referencia se indica. La salida debe indicar a que entidad en una base de conocimiento (Knowledge Base, KB) corresponde la mencion. En caso de no existir un referente apropiado la respuesta sera Not-in-KB (NIL). La tesis concluye con un analisis pormenorizado de los resultados obtenidos en la evaluacion.Postprint (published version

Developing Methods and Resources for Automated Processing of the African Language Igbo

Natural Language Processing (NLP) research is still in its infancy in Africa. Most of languages in Africa have few or zero NLP resources available, of which Igbo is among those at zero state. In this study, we develop NLP resources to support NLP-based research in the Igbo language. The springboard is the development of a new part-of-speech (POS) tagset for Igbo (IgbTS) based on a slight adaptation of the EAGLES guideline as a result of language internal features not recognized in EAGLES. The tagset consists of three granularities: fine-grain (85 tags), medium-grain (70 tags) and coarse-grain (15 tags). The medium-grained tagset is to strike a balance between the other two grains for practical purpose. Following this is the preprocessing of Igbo electronic texts through normalization and tokenization processes. The tokenizer is developed in this study using the tagset definition of a word token and the outcome is an Igbo corpus (IgbC) of about one million tokens. This IgbTS was applied to a part of the IgbC to produce the first Igbo tagged corpus (IgbTC). To investigate the effectiveness, validity and reproducibility of the IgbTS, an inter-annotation agreement (IAA) exercise was undertaken, which led to the revision of the IgbTS where necessary. A novel automatic method was developed to bootstrap a manual annotation process through exploitation of the by-products of this IAA exercise, to improve IgbTC. To further improve the quality of the IgbTC, a committee of taggers approach was adopted to propose erroneous instances on IgbTC for correction. A novel automatic method that uses knowledge of affixes to flag and correct all morphologically-inflected words in the IgbTC whose tags violate their status as not being morphologically-inflected was also developed and used. Experiments towards the development of an automatic POS tagging system for Igbo using IgbTC show good accuracy scores comparable to other languages that these taggers have been tested on, such as English. Accuracy on the words previously unseen during the taggers’ training (also called unknown words) is considerably low, and much lower on the unknown words that are morphologically-complex, which indicates difficulty in handling morphologically-complex words in Igbo. This was improved by adopting a morphological reconstruction method (a linguistically-informed segmentation into stems and affixes) that reformatted these morphologically-complex words into patterns learnable by machines. This enables taggers to use the knowledge of stems and associated affixes of these morphologically-complex words during the tagging process to predict their appropriate tags. Interestingly, this method outperforms other methods that existing taggers use in handling unknown words, and achieves an impressive increase for the accuracy of the morphologically-inflected unknown words and overall unknown words. These developments are the first NLP toolkit for the Igbo language and a step towards achieving the objective of Basic Language Resources Kits (BLARK) for the language. This IgboNLP toolkit will be made available for the NLP community and should encourage further research and development for the language

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Improving Search via Named Entity Recognition in Morphologically Rich Languages – A Case Study in Urdu

University of Minnesota Ph.D. dissertation. February 2018. Major: Computer Science. Advisors: Vipin Kumar, Blake Howald. 1 computer file (PDF); xi, 236 pages.Search is not a solved problem even in the world of Google and Bing's state of the art engines. Google and similar search engines are keyword based. Keyword-based searching suffers from the vocabulary mismatch problem -- the terms in document and user's information request don't overlap. For example, cars and automobiles. This phenomenon is called synonymy. Similarly, the user's term may be polysemous -- a user is inquiring about a river's bank, but documents about financial institutions are matched. Vocabulary mismatch exacerbated when the search occurs in Morphological Rich Language (MRL). Concept search techniques like dimensionality reduction do not improve search in Morphological Rich Languages. Names frequently occur news text and determine the "what," "where," "when," and "who" in the news text. Named Entity Recognition attempts to recognize names automatically in text, but these techniques are far from mature in MRL, especially in Arabic Script languages. Urdu is one the focus MRL of this dissertation among Arabic, Farsi, Hindi, and Russian, but it does not have the enabling technologies for NER and search. A corpus, stop word generation algorithm, a light stemmer, a baseline, and NER algorithm is created so the NER-aware search can be accomplished for Urdu. This dissertation demonstrates that NER-aware search on Arabic, Russian, Urdu, and English shows significant improvement over baseline. Furthermore, this dissertation highlights the challenges for researching in low-resource MRL languages

Anaphora resolution for Arabic machine translation :a case study of nafs

PhD ThesisIn the age of the internet, email, and social media there is an increasing need for processing online information, for example, to support education and business. This has led to the rapid development of natural language processing technologies such as computational linguistics, information retrieval, and data mining. As a branch of computational linguistics, anaphora resolution has attracted much interest. This is reflected in the large number of papers on the topic published in journals such as Computational Linguistics. Mitkov (2002) and Ji et al. (2005) have argued that the overall quality of anaphora resolution systems remains low, despite practical advances in the area, and that major challenges include dealing with real-world knowledge and accurate parsing. This thesis investigates the following research question: can an algorithm be found for the resolution of the anaphor nafs in Arabic text which is accurate to at least 90%, scales linearly with text size, and requires a minimum of knowledge resources? A resolution algorithm intended to satisfy these criteria is proposed. Testing on a corpus of contemporary Arabic shows that it does indeed satisfy the criteria.Egyptian Government

Joint Discourse-aware Concept Disambiguation and Clustering

This thesis addresses the tasks of concept disambiguation and clustering. Concept disambiguation is the task of linking common nouns and proper names in a text – henceforth called mentions – to their corresponding concepts in a predefined inventory. Concept clustering is the task of clustering mentions, so that all mentions in one cluster denote the same concept. In this thesis, we investigate concept disambiguation and clustering from a discourse perspective and propose a discourse-aware approach for joint concept disambiguation and clustering in the framework of Markov logic. The contributions of this thesis are fourfold: Joint Concept Disambiguation and Clustering. In previous approaches, concept disambiguation and concept clustering have been considered as two separate tasks (Schütze, 1998; Ji & Grishman, 2011). We analyze the relationship between concept disambiguation and concept clustering and argue that these two tasks can mutually support each other. We propose the – to our knowledge – first joint approach for concept disambiguation and clustering. Discourse-Aware Concept Disambiguation. One of the determining factors for concept disambiguation and clustering is the context definition. Most previous approaches use the same context definition for all mentions (Milne & Witten, 2008b; Kulkarni et al., 2009; Ratinov et al., 2011, inter alia). We approach the question which context is relevant to disambiguate a mention from a discourse perspective and state that different mentions require different notions of contexts. We state that the context that is relevant to disambiguate a mention depends on its embedding into discourse. However, how a mention is embedded into discourse depends on its denoted concept. Hence, the identification of the denoted concept and the relevant concept mutually depend on each other. We propose a binwise approach with three different context definitions and model the selection of the context definition and the disambiguation jointly. Modeling Interdependencies with Markov Logic. To model the interdependencies between concept disambiguation and concept clustering as well as the interdependencies between the context definition and the disambiguation, we use Markov logic (Domingos & Lowd, 2009). Markov logic combines first order logic with probabilities and allows us to concisely formalize these interdependencies. We investigate how we can balance between linguistic appropriateness and time efficiency and propose a hybrid approach that combines joint inference with aggregation techniques. Concept Disambiguation and Clustering beyond English: Multi- and Cross-linguality. Given the vast amount of texts written in different languages, the capability to extend an approach to cope with other languages than English is essential. We thus analyze how our approach copes with other languages than English and show that our approach largely scales across languages, even without retraining. Our approach is evaluated on multiple data sets originating from different sources (e.g. news, web) and across multiple languages. As an inventory, we use Wikipedia. We compare our approach to other approaches and show that it achieves state-of-the-art results. Furthermore, we show that joint concept disambiguating and clustering as well as joint context selection and disambiguation leads to significant improvements ceteris paribus

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

Automatizované metody popisu struktury odborného textu a vztah některých prvků ke kvalitě textu

Universal Semantic Language (USL) is a semi-formalized approach for the description of knowledge (a knowledge representation tool). The idea of USL was introduced by Vladimir Smetacek in the system called SEMAN which was used for keyword extraction tasks in the former Information centre of the Czechoslovak Republic. However due to the dissolution of the centre in early 90's, the system has been lost. This thesis reintroduces the idea of USL in a new context of quantitative content analysis. First we introduce the historical background and the problems of semantics and knowledge representation, semes, semantic fields, semantic primes and universals. The basic methodology of content analysis studies is illustrated on the example of three content analysis tools and we describe the architecture of a new system. The application was built specifically for USL discovery but it can work also in the context of classical content analysis. It contains Natural Language Processing (NLP) components and employs the algorithm for collocation discovery adapted for the case of cooccurences search between semantic annotations. The software is evaluated by comparing its pattern matching mechanism against another existing and established extractor. The semantic translation mechanism is evaluated in the task of...Univerzální sémantický jazyk (USJ) je semi-formalizovaný způsob zápisu znalostí (systém pro reprezentaci znalostí). Myšlenka USJ byla rozvinuta Vladimírem Smetáčkem v 80. letech při pracech na systému SÉMAN (Universální semantický analyzátor). Tento systém byl využíván pro automatizovanou extrakci klíčových slov v tehdejším informačním centru ČSSR. Avšak se zánikem centra v 90. letech byl systém SEMAN ztracen. Tato dizertace oživuje myšlenku USJ v novém kontextu automatizované obsahové analýzy. Nejdříve prezentujeme historický kontext a problémy spojené s reprezentací znalostí, sémů, sémantických polí, sémantických primitivů a univerzálií. Dále je představena metodika kvantitativní obsahové analýzy na příkladu tří klasických aplikací. Podrobně popíšeme architekturu nové aplikace, která byla vyvinuta speciálně pro potřeby evaluace USJ. Program může fungovat jako nástroj pro klasickou obsahovou analýzu, avšak obsahuje i nástroje pro zpracování přirozeného jazyka (NLP) a využívá algoritmů pro vyhledávání kolokací. Tyto byly upraveny pro potřeby vyhledávání vazeb mezi sémantickými anotacemi. Jednotlivé součásti programu jsou podrobeny praktickým testům. Subsystém pro vyhledávní vzorů v textech je porovnán s existujícím extraktorem klíčových slov. Mechanismus pro překlad do sémantických kódů je...Institute of Information Studies and LibrarianshipÚstav informačních studií a knihovnictvíFilozofická fakultaFaculty of Art