1,044 research outputs found

    Understanding comparative questions and retrieving argumentative answers

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    Making decisions is an integral part of everyday life, yet it can be a difficult and complex process. While peoples’ wants and needs are unlimited, resources are often scarce, making it necessary to research the possible alternatives and weigh the pros and cons before making a decision. Nowadays, the Internet has become the main source of information when it comes to comparing alternatives, making search engines the primary means for collecting new information. However, relying only on term matching is not sufficient to adequately address requests for comparisons. Therefore, search systems should go beyond this approach to effectively address comparative information needs. In this dissertation, I explore from different perspectives how search systems can respond to comparative questions. First, I examine approaches to identifying comparative questions and study their underlying information needs. Second, I investigate a methodology to identify important constituents of comparative questions like the to-be-compared options and to detect the stance of answers towards these comparison options. Then, I address ambiguous comparative search queries by studying an interactive clarification search interface. And finally, addressing answering comparative questions, I investigate retrieval approaches that consider not only the topical relevance of potential answers but also account for the presence of arguments towards the comparison options mentioned in the questions. By addressing these facets, I aim to provide a comprehensive understanding of how to effectively satisfy the information needs of searchers seeking to compare different alternatives

    Entity Linking for the Biomedical Domain

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    Entity linking is the process of detecting mentions of different concepts in text documents and linking them to canonical entities in a target lexicon. However, one of the biggest issues in entity linking is the ambiguity in entity names. The ambiguity is an issue that many text mining tools have yet to address since different names can represent the same thing and every mention could indicate a different thing. For instance, search engines that rely on heuristic string matches frequently return irrelevant results, because they are unable to satisfactorily resolve ambiguity. Thus, resolving named entity ambiguity is a crucial step in entity linking. To solve the problem of ambiguity, this work proposes a heuristic method for entity recognition and entity linking over the biomedical knowledge graph concerning the semantic similarity of entities in the knowledge graph. Named entity recognition (NER), relation extraction (RE), and relationship linking make up a conventional entity linking (EL) system pipeline (RL). We have used the accuracy metric in this thesis. Therefore, for each identified relation or entity, the solution comprises identifying the correct one and matching it to its corresponding unique CUI in the knowledge base. Because KBs contain a substantial number of relations and entities, each with only one natural language label, the second phase is directly dependent on the accuracy of the first. The framework developed in this thesis enables the extraction of relations and entities from the text and their mapping to the associated CUI in the UMLS knowledge base. This approach derives a new representation of the knowledge base that lends it to the easy comparison. Our idea to select the best candidates is to build a graph of relations and determine the shortest path distance using a ranking approach. We test our suggested approach on two well-known benchmarks in the biomedical field and show that our method exceeds the search engine's top result and provides us with around 4% more accuracy. In general, when it comes to fine-tuning, we notice that entity linking contains subjective characteristics and modifications may be required depending on the task at hand. The performance of the framework is evaluated based on a Python implementation

    Efficient and Explainable Neural Ranking

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    The recent availability of increasingly powerful hardware has caused a shift from traditional information retrieval (IR) approaches based on term matching, which remained the state of the art for several decades, to large pre-trained neural language models. These neural rankers achieve substantial improvements in performance, as their complexity and extensive pre-training give them the ability of understanding natural language in a way. As a result, neural rankers go beyond term matching by performing relevance estimation based on the semantics of queries and documents. However, these improvements in performance don't come without sacrifice. In this thesis, we focus on two fundamental challenges of neural ranking models, specifically, ones based on large language models: On the one hand, due to their complexity, the models are inefficient; they require considerable amounts of computational power, which often comes in the form of specialized hardware, such as GPUs or TPUs. Consequently, the carbon footprint is an increasingly important aspect of systems using neural IR. This effect is amplified when low latency is required, as in, for example, web search. On the other hand, neural models are known for being inherently unexplainable; in other words, it is often not comprehensible for humans why a neural model produced a specific output. In general, explainability is deemed important in order to identify undesired behavior, such as bias. We tackle the efficiency challenge of neural rankers by proposing Fast-Forward indexes, which are simple vector forward indexes that heavily utilize pre-computation techniques. Our approach substantially reduces the computational load during query processing, enabling efficient ranking solely on CPUs without requiring hardware acceleration. Furthermore, we introduce BERT-DMN to show that the training efficiency of neural rankers can be improved by training only parts of the model. In order to improve the explainability of neural ranking, we propose the Select-and-Rank paradigm to make ranking models explainable by design: First, a query-dependent subset of the input document is extracted to serve as an explanation; second, the ranking model makes its decision based only on the extracted subset, rather than the complete document. We show that our models exhibit performance similar to models that are not explainable by design and conduct a user study to determine the faithfulness of the explanations. Finally, we introduce BoilerNet, a web content extraction technique that allows the removal of boilerplate from web pages, leaving only the main content in plain text. Our method requires no feature engineering and can be used to aid in the process of creating new document corpora from the web

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    Continuous Rationale Management

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    Continuous Software Engineering (CSE) is a software life cycle model open to frequent changes in requirements or technology. During CSE, software developers continuously make decisions on the requirements and design of the software or the development process. They establish essential decision knowledge, which they need to document and share so that it supports the evolution and changes of the software. The management of decision knowledge is called rationale management. Rationale management provides an opportunity to support the change process during CSE. However, rationale management is not well integrated into CSE. The overall goal of this dissertation is to provide workflows and tool support for continuous rationale management. The dissertation contributes an interview study with practitioners from the industry, which investigates rationale management problems, current practices, and features to support continuous rationale management beneficial for practitioners. Problems of rationale management in practice are threefold: First, documenting decision knowledge is intrusive in the development process and an additional effort. Second, the high amount of distributed decision knowledge documentation is difficult to access and use. Third, the documented knowledge can be of low quality, e.g., outdated, which impedes its use. The dissertation contributes a systematic mapping study on recommendation and classification approaches to treat the rationale management problems. The major contribution of this dissertation is a validated approach for continuous rationale management consisting of the ConRat life cycle model extension and the comprehensive ConDec tool support. To reduce intrusiveness and additional effort, ConRat integrates rationale management activities into existing workflows, such as requirements elicitation, development, and meetings. ConDec integrates into standard development tools instead of providing a separate tool. ConDec enables lightweight capturing and use of decision knowledge from various artifacts and reduces the developers' effort through automatic text classification, recommendation, and nudging mechanisms for rationale management. To enable access and use of distributed decision knowledge documentation, ConRat defines a knowledge model of decision knowledge and other artifacts. ConDec instantiates the model as a knowledge graph and offers interactive knowledge views with useful tailoring, e.g., transitive linking. To operationalize high quality, ConRat introduces the rationale backlog, the definition of done for knowledge documentation, and metrics for intra-rationale completeness and decision coverage of requirements and code. ConDec implements these agile concepts for rationale management and a knowledge dashboard. ConDec also supports consistent changes through change impact analysis. The dissertation shows the feasibility, effectiveness, and user acceptance of ConRat and ConDec in six case study projects in an industrial setting. Besides, it comprehensively analyses the rationale documentation created in the projects. The validation indicates that ConRat and ConDec benefit CSE projects. Based on the dissertation, continuous rationale management should become a standard part of CSE, like automated testing or continuous integration

    Survey of Vector Database Management Systems

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    There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. Driving this shift from algorithms to systems are new data intensive applications, notably large language models, that demand vast stores of unstructured data coupled with reliable, secure, fast, and scalable query processing capability. A variety of new data management techniques now exist for addressing these needs, however there is no comprehensive survey to thoroughly review these techniques and systems. We start by identifying five main obstacles to vector data management, namely vagueness of semantic similarity, large size of vectors, high cost of similarity comparison, lack of natural partitioning that can be used for indexing, and difficulty of efficiently answering hybrid queries that require both attributes and vectors. Overcoming these obstacles has led to new approaches to query processing, storage and indexing, and query optimization and execution. For query processing, a variety of similarity scores and query types are now well understood; for storage and indexing, techniques include vector compression, namely quantization, and partitioning based on randomization, learning partitioning, and navigable partitioning; for query optimization and execution, we describe new operators for hybrid queries, as well as techniques for plan enumeration, plan selection, and hardware accelerated execution. These techniques lead to a variety of VDBMSs across a spectrum of design and runtime characteristics, including native systems specialized for vectors and extended systems that incorporate vector capabilities into existing systems. We then discuss benchmarks, and finally we outline research challenges and point the direction for future work.Comment: 25 page

    Performance Challenges with Data Visualizations in Browser Environment

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    Information exists in many forms, from text, to equations, videos, audio, and graphical mediums. With graphical or visual mediums, it is becoming easier to absorb information where the alternatives are textual descriptions. Graphs are important vehicles of transporting information. In order to create a good graph, certain attributes need to be taken into account, such as which variables are being displayed over which axis, visual elements, and their sizes are also important to consider. In modern times with the internet and the amount of data being generated, how can all this data be fitted into a single graph? That question is the motivation for this thesis. Presenting large data in visualizations involves a great deal of thought, effort, and ingenuity on how to proceed with what information to convey. There are times when obtaining data for such visualization come with their own challenges. This thesis investigates the obstacles facing an internal tool within a company in regard to their data retrieval method. As well as the objective to research an efficient and easy-to-use method for presenting large data on a webpage

    Intelligent Software Tooling For Improving Software Development

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    Software has eaten the world with many of the necessities and quality of life services people use requiring software. Therefore, tools that improve the software development experience can have a significant impact on the world such as generating code and test cases, detecting bugs, question and answering, etc. The success of Deep Learning (DL) over the past decade has shown huge advancements in automation across many domains, including Software Development processes. One of the main reasons behind this success is the availability of large datasets such as open-source code available through GitHub or image datasets of mobile Graphical User Interfaces (GUIs) with RICO and ReDRAW to be trained on. Therefore, the central research question my dissertation explores is: In what ways can the software development process be improved through leveraging DL techniques on the vast amounts of unstructured software engineering artifacts? We coin the approaches that leverage DL to automate or augment various software development task as Intelligent Software Tools. To guide our research of these intelligent software tools, we performed a systematic literature review to understand the current landscape of research on applying DL techniques to software tasks and any gaps that exist. From this literature review, we found code generation to be one of the most studied tasks with other tasks and artifacts such as impact analysis or tasks involving images and videos to be understudied. Therefore, we set out to explore the application of DL to these understudied tasks and artifacts as well as the limitations of DL models under the well studied task code completion, a subfield in code generation. Specifically, we developed a tool for automatically detecting duplicate mobile bug reports from user submitted videos. We used the popular Convolutional Neural Network (CNN) to learn important features from a large collection of mobile screenshots. Using this model, we could then compute similarity between a newly submitted bug report and existing ones to produce a ranked list of duplicate candidates that can be reviewed by a developer. Next, we explored impact analysis, a critical software maintenance task that identifies potential adverse effects of a given code change on the larger software system. To this end, we created Athena, a novel approach to impact analysis that integrates knowledge of a software system through its call-graph along with high-level representations of the code inside the system to improve impact analysis performance. Lastly, we explored the task of code completion, which has seen heavy interest from industry and academia. Specifically, we explored various methods that modify the positional encoding scheme of the Transformer architecture for allowing these models to incorporate longer sequences of tokens when predicting completions than seen during their training as this can significantly improve training times

    FVI-BD: Multiple File Extraction using Fusion Vector Investigation (FVI) in Big Data Hadoop Environment

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    — The Information Extraction (IE) approach extracts useful data from unstructured and semi-structured data. Big Data, with its rising volume of multidimensional unstructured data, provides new tools for IE. Traditional Information Extraction (IE) systems are incapable of appropriately handling this massive flood of unstructured data. The processing capability of current IE systems must be enhanced because to the amount and variety of Big Data. Existing IE techniques for data preparation, extraction, and transformation, as well as representations of massive amounts of multidimensional, unstructured data, must be evaluated in terms of their capabilities and limits. The proposed FVI-BD Framework for IOT device Information Extraction in Big Data. The unstructured data has cleaned and integration using POS tagging and similarity finding using LTA method. The features are extracted using TF and IDF. The Information extracted using NLP with WordNet. The classification has done with FVI algorithm.  This research paper discovered that vast data analytics may be enhanced by extracting document feature terms with synonymous similarity and increasing IE accuracy
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