The advent of the era of big data on the Web has made automatic web information extraction an essential tool in data acquisition processes. Unfortunately, automated solutions are in most cases more error prone than those created by humans, resulting in dirty and erroneous data. Automatic repair and cleaning of the extracted data is thus a necessary complement to information extraction on the Web. This thesis investigates the problem of inducing cleaning rules on web extracted data in order to (i) repair and align the data w.r.t. an original target schema, (ii) produce repairs that are as generic as possible such that different instances can benefit from them. The problem is addressed from three different angles: replace cross-site redundancy with an ensemble of entity recognisers; produce general repairs that can be encoded in the extraction process; and exploit entity-wide relations to infer common knowledge on extracted data. First, we present ROSeAnn, an unsupervised approach to integrate semantic annotators and produce a unied and consistent annotation layer on top of them. Both the diversity in vocabulary and widely varying accuracy justify the need for middleware that reconciles different annotator opinions. Considering annotators as "black-boxes" that do not require per-domain supervision allows us to recognise semantically related content in web extracted data in a scalable way. Second, we show in WADaR how annotators can be used to discover rules to repair web extracted data. We study the problem of computing joint repairs for web data extraction programs and their extracted data, providing an approximate solution that requires no per-source supervision and proves effective across a wide variety of domains and sources. The proposed solution is effective not only in repairing the extracted data, but also in encoding such repairs in the original extraction process. Third, we investigate how relationships among entities can be exploited to discover inconsistencies and additional information. We present RuDiK, a disk-based scalable solution to discover first-order logic rules over RDF knowledge bases built from web sources. We present an approach that does not limit its search space to rules that rely on "positive" relationships between entities, as in the case with traditional mining of constraints. On the contrary, it extends the search space to also discover negative rules, i.e., patterns that lead to contradictions in the data.

This thesis describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. The reason for working with documents, as opposed to for instance sentences or phrases, is that the BootMark method is concerned with the creation of corpora. The claim made in the thesis is that BootMark requires a human annotator to manually annotate fewer documents in order to produce a named entity recognizer with a given performance, than would be needed if the documents forming the basis for the recognizer were randomly drawn from the same corpus. The intention is then to use the created named en- tity recognizer as a pre-tagger and thus eventually turn the manual annotation process into one in which the annotator reviews system-suggested annotations rather than creating new ones from scratch. The BootMark method consists of three phases: (1) Manual annotation of a set of documents; (2) Bootstrapping – active machine learning for the purpose of selecting which document to an- notate next; (3) The remaining unannotated documents of the original corpus are marked up using pre-tagging with revision. Five emerging issues are identified, described and empirically investigated in the thesis. Their common denominator is that they all depend on the real- ization of the named entity recognition task, and as such, require the context of a practical setting in order to be properly addressed. The emerging issues are related to: (1) the characteristics of the named entity recognition task and the base learners used in conjunction with it; (2) the constitution of the set of documents annotated by the human annotator in phase one in order to start the bootstrapping process; (3) the active selection of the documents to annotate in phase two; (4) the monitoring and termination of the active learning carried out in phase two, including a new intrinsic stopping criterion for committee-based active learning; and (5) the applicability of the named entity recognizer created during phase two as a pre-tagger in phase three. The outcomes of the empirical investigations concerning the emerging is- sues support the claim made in the thesis. The results also suggest that while the recognizer produced in phases one and two is as useful for pre-tagging as a recognizer created from randomly selected documents, the applicability of the recognizer as a pre-tagger is best investigated by conducting a user study involving real annotators working on a real named entity recognition task.

This dissertation addresses the problem of classification of entities in text represented by noun phrases. The goal of this thesis is to develop a method for automated classification of entities appearing in datasets consisting of short textual fragments. The emphasis is on unsupervised and semi-supervised methods that will allow for fine-grained character of the assigned classes and require no labeled instances for training. The set of target classes is either user-defined or determined automatically. Our initial attempt to address the entity classification problem is called Semantic Concept Mapping (SCM) algorithm. SCM maps the noun phrases representing the entities as well as the target classes to WordNet. Graph-based WordNet similarity measures are used to assign the closest class to the noun phrase. If a noun phrase does not match any WordNet concept, a Targeted Hypernym Discovery (THD) algorithm is executed. The THD algorithm extracts a hypernym from a Wikipedia article defining the noun phrase using lexico-syntactic patterns. This hypernym is then used to map the noun phrase to a WordNet synset, but it can also be perceived as the classification result by itself, resulting in an unsupervised classification system. SCM and THD algorithms were designed for English. While adaptation of these algorithms for other languages is conceivable, we decided to develop the Bag of Articles (BOA) algorithm, which is language agnostic as it is based on the statistical Rocchio classifier. Since this algorithm utilizes Wikipedia as a source of data for classification, it does not require any labeled training instances. WordNet is used in a novel way to compute term weights. It is also used as a positive term list and for lemmatization. A disambiguation algorithm utilizing global context is also proposed. We consider the BOA algorithm to be the main contribution of this dissertation. Experimental evaluation of the proposed algorithms is performed on the WordSim353 dataset, which is used for evaluation in the Word Similarity Computation (WSC) task, and on the Czech Traveler dataset, the latter being specifically designed for the purpose of our research. BOA performance on WordSim353 achieves Spearman correlation of 0.72 with human judgment, which is close to the 0.75 correlation for the ESA algorithm, to the author's knowledge the best performing algorithm for this gold-standard dataset, which does not require training data. The advantage of BOA over ESA is that it has smaller requirements on preprocessing of the Wikipedia data. While SCM underperforms on the WordSim353 dataset, it overtakes BOA on the Czech Traveler dataset, which was designed specifically for our entity classification problem. This discrepancy requires further investigation. In a standalone evaluation of THD on Czech Traveler dataset the algorithm returned a correct hypernym for 62% of entities.

This master thesis deals with the extraction of relationships between named entities in the text. In the theoretical part of the thesis, the issue of natural language representation for machine processing is discussed. Subsequently, two partial tasks of relationship extraction are defined, namely named entities recognition and classification of relationships between them, including a summary of state-of-the-art solutions. In the practical part of the thesis, system for automatic extraction of relationships between named entities from downloaded pages is designed. The classification of relationships between entities is based on the pre-trained transformers. In this thesis, four pre-trained transformers are compared, namely BERT, XLNet, RoBERTa and ALBERT.

In this master thesis are described the history and theoretical background of named-entity recognition and implementation of the system in C++ for named entity recognition and disambiguation. The system uses local disambiguation method and statistics generated from the  Wikilinks web dataset. With implemented system and with alternative implementations are performed various experiments and tests. These experiments show that the system is sufficiently successful and fast. System participates in the Entity Recognition and Disambiguation Challenge 2014.

Today's communication between operators and professional drivers takes place through direct conversations between the parties. This thesis project explores the possibility to support the operators in classifying the topic of incoming communications and which entities are affected through the use of named entity recognition and topic classifications. By developing a synthetic training dataset, a NER model and a topic classification model was developed and evaluated to achieve F1-scores of 71.4 and 61.8 respectively. These results were explained by a low variance in the synthetic dataset in comparison to a transcribed dataset from the real world which included anomalies not represented in the synthetic dataset. The aforementioned models were integrated into the dialogue framework Emora to seamlessly handle the back and forth communication and generating responses.

Transformer-based architectures have in recent years advanced state-of-the-art performance in Natural Language Processing. Researchers have successfully adapted such models to downstream tasks within NLP in a domain-specific setting. This thesis examines the application of these models to the legal domain by doing Named Entity Recognition (NER) in a setting of scarce training data. Three different pre-trained BERT models are fine-tuned on a set of 101 court case documents, whereof one model is pre-trained on legal corpora and the other two on general corpora. Experiments are run to evaluate the models’ predictive performance given smaller or larger quantities of data to fine-tune on. Results show that BERT models work reasonably well for NER with legal data. Unlike many other domain-specific BERT models, the BERT model trained on legal corpora does not outperform the base models. Modest amounts of annotated data seem sufficient for reasonably good performance.

[pt] Este trabalho tem como objetivo identificar uma terminologia e expressões relevantes do domínio de óleo e gás (OeG) e estruturá-la como uma taxonomia, tendo em vista o levantamento de itens para a anotação de entidades dentro do domínio. Para tanto, foi construída uma lista de termos relevantes da área, com base em diversas fontes, e, em seguida, a lista foi estruturada hierarquicamente por meio de regras. O processo de elaboração da taxonomia seguiu aspectos teóricometodológicos utilizados por diversos trabalhos semelhantes dentro da área. O trabalho procura evidenciar que a identificação de uma terminologia de um domínio técnico e a sua estruturação como taxonomia podem servir como a primeira etapa do levantamento de entidades de um domínio. Por conta disso, o trabalho também se propõe a discutir estratégias para identificação de entidade mencionada (EM) e possibilitar um diálogo entre duas áreas: Processamento de Linguagem Natural (PLN) e Linguística. De maneira geral, espera-se que a taxonomia ajudar a suprir, mesmo que de forma modesta, a escassez de recursos linguísticos para as técnicas do Processamento de Linguagem Natural (PLN) e da Extração de Informação (EI), dentro da área de óleo e gás. / [en] This work aims to identify a terminology and relevant expressions of the oil and gas domain and structure it as a taxonomy. To this end, a list of relevant terms in the area was built, based on various sources, and then the list was structured hierarchically by rules. The taxonomy elaboration process followed theoretical and methodological aspects used by several similar works within the area. The work tries to show that the identification of a technical domain terminology and its structuring as a taxonomy can serve as the first stage of the identification of entities in a domain. Because of this, the work also proposes to discuss strategies for identifying named entity and to enable a dialogue between two areas: Natural Language Processing (NLP) and Linguistics. In general, the taxonomy presented is expected to supply, at least in a modest way, the lack of linguistic resources for techniques of Natural Language Processing (NLP) and Information Extraction (EI), within the area of oil and gas.

This master thesis explores the effectiveness of interpolating a larger generic speech recognition model with smaller domain-specific models to enable transcription of domain-specific conversations. The study uses a corpus within the financial domain collected from the web and processed by abstracting named entities such as financial instruments, numbers, as well as names of people and companies. By substituting each named entity with a tag representing the entity type in the domain-specific corpus, each named entity can be replaced during the hypothesis search by words added to the systems pronunciation dictionary. Thus making instruments and other domain-specific terms a matter of extension by configuration.  A proof-of-concept automatic speech recognition system with the ability to transcribe and extract named entities within the constantly changing domain of voice trading was created. The system achieved a 25.08 Word Error Rate and 0.9091 F1-score using stochastic and neural net based language models. The best configuration proved to be a combination of both stochastic and neural net based domain-specific models interpolated with a generic model. This shows that even though the models were trained using the same corpus, different models learned different aspects of the material. The study was deemed successful by the authors as the Word Error Rate was improved by model interpolation and all but one named entities were found in the test recordings by all configurations. By adjusting the amount of influence the domain-specific models had against the generic model, the results improved the transcription accuracy at the cost of named entity recognition, and vice versa. Ultimately, the choice of configuration depends on the business case and the importance of named entity recognition versus accurate transcriptions.

This thesis addresses the problem of named entity recognition (NER) in music-related search queries. NER is the task of identifying keywords in text and classifying them into predefined categories. Previous work in the field has mainly focused on longer documents of editorial texts. However, in recent years, the application of NER for queries has attracted increased attention. This task is, however, acknowledged to be challenging due to queries being short, ungrammatical and containing minimal linguistic context. The usage of NER for queries is especially useful for the implementation of natural language queries in domain-specific search applications. These applications are often backed by a database, where the query format otherwise is restricted to keyword search or the usage of a formal query language. In this thesis, two techniques for NER for music-related queries are evaluated; a conditional random field based solution and a probabilistic solution based on context words. As a baseline, the most elementary implementation of NER, commonly applied on editorial text, is used. Both of the evaluated approaches outperform the baseline and demonstrate an overall F1 score of 79.2% and 63.4% respectively. The experimental results show a high precision for the probabilistic approach and the conditional random field based solution demonstrates an F1 score comparable to previous studies from other domains. / Denna avhandling redogör för identifiering av namngivna enheter i musikrelaterade sökfrågor. Identifiering av namngivna enheter innebär att extrahera nyckelord från text och att klassificera dessa till någon av ett antal förbestämda kategorier. Tidigare forskning kring ämnet har framför allt fokuserat på längre redaktionella dokument. Däremot har intresset för tillämpningar på sökfrågor ökat de senaste åren. Detta anses vara ett svårt problem då sökfrågor i allmänhet är korta, grammatiskt inkorrekta och innehåller minimal språklig kontext. Identifiering av namngivna enheter är framför allt användbart för domänspecifika sökapplikationer där målet är att kunna tolka sökfrågor skrivna med naturligt språk. Dessa applikationer baseras ofta på en databas där formatet på sökfrågorna annars är begränsat till att enbart använda nyckelord eller användande av ett formellt frågespråk. I denna avhandling har två tekniker för identifiering av namngivna enheter för musikrelaterade sökfrågor undersökts; en metod baserad på villkorliga slumpfält (eng. conditional random field) och en probabilistisk metod baserad på kontextord. Som baslinje har den mest grundläggande implementationen, som vanligtvis används för redaktionella texter, valts. De båda utvärderade metoderna presterar bättre än baslinjen och ges ett F1-värde på 79,2% respektive 63,4%. De experimentella resultaten visar en hög precision för den probabilistiska implementationen och metoden ba- serad på villkorliga slumpfält visar på resultat på en nivå jämförbar med tidigare studier inom andra domäner.