A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model

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

Learning natural coding conventions

Coding conventions are ubiquitous in software engineering practice. Maintaining a uniform coding style allows software development teams to communicate through code by making the code clear and, thus, readable and maintainable—two important properties of good code since developers spend the majority of their time maintaining software systems. This dissertation introduces a set of probabilistic machine learning models of source code that learn coding conventions directly from source code written in a mostly conventional style. This alleviates the coding convention enforcement problem, where conventions need to first be formulated clearly into unambiguous rules and then be coded in order to be enforced; a tedious and costly process. First, we introduce the problem of inferring a variable’s name given its usage context and address this problem by creating Naturalize — a machine learning framework that learns to suggest conventional variable names. Two machine learning models, a simple n-gram language model and a specialized neural log-bilinear context model are trained to understand the role and function of each variable and suggest new stylistically consistent variable names. The neural log-bilinear model can even suggest previously unseen names by composing them from subtokens (i.e. sub-components of code identifiers). The suggestions of the models achieve 90% accuracy when suggesting variable names at the top 20% most confident locations, rendering the suggestion system usable in practice. We then turn our attention to the significantly harder method naming problem. Learning to name methods, by looking only at the code tokens within their body, requires a good understating of the semantics of the code contained in a single method. To achieve this, we introduce a novel neural convolutional attention network that learns to generate the name of a method by sequentially predicting its subtokens. This is achieved by focusing on different parts of the code and potentially directly using body (sub)tokens even when they have never been seen before. This model achieves an F1 score of 51% on the top five suggestions when naming methods of real-world open-source projects. Learning about naming code conventions uses the syntactic structure of the code to infer names that implicitly relate to code semantics. However, syntactic similarities and differences obscure code semantics. Therefore, to capture features of semantic operations with machine learning, we need methods that learn semantic continuous logical representations. To achieve this ambitious goal, we focus our investigation on logic and algebraic symbolic expressions and design a neural equivalence network architecture that learns semantic vector representations of expressions in a syntax-driven way, while solely retaining semantics. We show that equivalence networks learn significantly better semantic vector representations compared to other, existing, neural network architectures. Finally, we present an unsupervised machine learning model for mining syntactic and semantic code idioms. Code idioms are conventional “mental chunks” of code that serve a single semantic purpose and are commonly used by practitioners. To achieve this, we employ Bayesian nonparametric inference on tree substitution grammars. We present a wide range of evidence that the resulting syntactic idioms are meaningful, demonstrating that they do indeed recur across software projects and that they occur more frequently in illustrative code examples collected from a Q&A site. These syntactic idioms can be used as a form of automatic documentation of coding practices of a programming language or an API. We also mine semantic loop idioms, i.e. highly abstracted but semantic-preserving idioms of loop operations. We show that semantic idioms provide data-driven guidance during the creation of software engineering tools by mining common semantic patterns, such as candidate refactoring locations. This gives data-based evidence to tool, API and language designers about general, domain and project-specific coding patterns, who instead of relying solely on their intuition, can use semantic idioms to achieve greater coverage of their tool or new API or language feature. We demonstrate this by creating a tool that suggests loop refactorings into functional constructs in LINQ. Semantic loop idioms also provide data-driven evidence for introducing new APIs or programming language features

Intelligent Information Access to Linked Data - Weaving the Cultural Heritage Web

The subject of the dissertation is an information alignment experiment of two cultural heritage information systems (ALAP): The Perseus Digital Library and Arachne. In modern societies, information integration is gaining importance for many tasks such as business decision making or even catastrophe management. It is beyond doubt that the information available in digital form can offer users new ways of interaction. Also, in the humanities and cultural heritage communities, more and more information is being published online. But in many situations the way that information has been made publicly available is disruptive to the research process due to its heterogeneity and distribution. Therefore integrated information will be a key factor to pursue successful research, and the need for information alignment is widely recognized. ALAP is an attempt to integrate information from Perseus and Arachne, not only on a schema level, but to also perform entity resolution. To that end, technical peculiarities and philosophical implications of the concepts of identity and co-reference are discussed. Multiple approaches to information integration and entity resolution are discussed and evaluated. The methodology that is used to implement ALAP is mainly rooted in the fields of information retrieval and knowledge discovery. First, an exploratory analysis was performed on both information systems to get a first impression of the data. After that, (semi-)structured information from both systems was extracted and normalized. Then, a clustering algorithm was used to reduce the number of needed entity comparisons. Finally, a thorough matching was performed on the different clusters. ALAP helped with identifying challenges and highlighted the opportunities that arise during the attempt to align cultural heritage information systems

Numeracy of Language Models: Joint Modelling of Words and Numbers

Numeracy and literacy are the abilities to understand and work with numbers and words, respectively. While both skills are necessary for reading and writing documents in clinical, scientific, and other technical domains, existing statistical language models focus on words to the expense of numbers: numbers are ignored, masked, or treated similarly to words, which can obscure numerical content and cause sparsity issues, e.g. high out-of-vocabulary rates. In this thesis, we investigate whether the performance of neural language models can be improved by i) considering numerical information as additional inputs and ii) explicitly modelling the output of numerical tokens. In experiments with numbers as input, we find that numerical input features improve perplexity by 33% on a clinical dataset. In assisted text entry and verification tasks, numerical input features improve recall from 25.03% to 71.28% for word prediction with a list of 5 suggestions, keystroke savings from 34.35% to 44.81% for word completion, and F1 metric by 5 points for semantic error correction. Numerical information from an accompanying knowledge base helps improve performance further. In experiments with numerical tokens as output, we consider different strategies, e.g. memorisation and digit-by-digit composition, and propose a novel neural component based on Gaussian mixture density estimation. We propose the use of regression metrics to evaluate numerical accuracy and an adjusted perplexity metric that accounts for the high out-of-vocabulary rate of numerals. Our evaluation on clinical and scientific datasets shows that perplexity can be improved by more than 2 and 4 orders of magnitude, respectively, by modelling words and numerals with different sub-models through a hierarchical softmax. For the same datasets, our proposed mixture of Gaussians model achieved a 32% and 54% reduction of mean average percentage errors over the contender strategy, digit-by-digit composition. We conclude with a critical reflection of this thesis and suggestions for future work

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Scalable deep learning for bug detection

The application of machine learning (ML) and natural language processing (NLP) methods for creating software engineering (SE) tools is a recent emerging trend. A crucial early decision is how to model software’s vocabulary. Unlike in natural language, software developers are free to create any identifiers they like, and can make them arbitrarily complex resulting in an immense out of vocabulary problem. This fundamental fact prohibits training of Neural models on large-scale software corpora. This thesis aimed on addressing this problem. As an initial step we studied the most common ways for vocabulary reduction previously considered in the software engineering literature and found that they are not enough to obtain a vocabulary of manageable size. Instead this goal was reached by using an adaptation of the Byte-Pair Encoding (BPE) algorithm, which produces an open-vocabulary neural language model (NLM). Experiments on large corpora show that the resulting NLM outperforms other LMs both in perplexity and code completion performance for several programming languages. It continues by showing that the improvement in language modelling transfers to downstream SE tasks by finding that the BPE NLMs are more effective in highlighting buggy code than previous LMs. Driven by this finding and from recent advances in NLP it also investigates the idea of transferring language model representations to program repair systems. Program repair is an important but difficult software engineering problem. One way to achieve a “sweet spot” of low false positive rates, while maintaining high enough recall to be usable, is to focus on repairing classes of simple bugs, such as bugs with single statement fixes, or that match a small set of bug templates. However, it is very difficult to estimate the recall of repair techniques based on templates or based on repairing simple bugs, as there are no datasets about how often the associated bugs occur in code. To fill this gap, the thesis contributes a large dataset of single statement Java bug-fix changes annotated by whether they match any of a set of 16 bug templates along with a methodology for mining similar datasets. These specific patterns were selected with the criteria that they appear often in open-source Java code and relate to those used by mutation and pattern-based repair tools. They also aim at extracting bugs that compile both before and after repair as such can be quite tedious to manually spot, yet their fixes are simple. These mined bugs are quite frequent appearing about every 2000 lines of code and that their fixes are very often already present in the code satisfying the popular plastic surgery hypothesis. Furthermore, it introduces a hypothesis that contextual embeddings offer potential modelling advantages that are specifically suited for modelling source code due to its nature. Contextual embeddings are common in natural language processing but have not been previously applied in software engineering. As such another contribution is the introduction a new set of deep contextualized word representations for computer programs based on the ELMo (embeddings from language models) framework of Peters et al (2018). It is shown that even a low-dimensional embedding trained on a relatively small corpus of programs can improve a state-of-the-art machine learning system for bug detection of single statement fixes. The systems were evaluated on the DeepBugs dataset of synthetic bugs, a new synthetic test dataset, and a small dataset of real JavaScript bugs. Lastly, the final contribution is the first steps at answering whether neural bug-finding is useful in practice by performing an evaluation study over a small set of real bugs