176 research outputs found
Workshop Proceedings of the 12th edition of the KONVENS conference
The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding
Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million technical drawings with 132,890 object names and 22,394 viewpoints extracted from 14 years of US design patent documents. We demonstrate the usefulness of DeepPatent2 with conceptual captioning. We further provide the potential usefulness of our dataset to facilitate other research areas such as 3D image reconstruction and image retrieval
Perspectives on Large Language Models for Relevance Judgment
When asked, current large language models (LLMs) like ChatGPT claim that they
can assist us with relevance judgments. Many researchers think this would not
lead to credible IR research. In this perspective paper, we discuss possible
ways for LLMs to assist human experts along with concerns and issues that
arise. We devise a human-machine collaboration spectrum that allows
categorizing different relevance judgment strategies, based on how much the
human relies on the machine. For the extreme point of "fully automated
assessment", we further include a pilot experiment on whether LLM-based
relevance judgments correlate with judgments from trained human assessors. We
conclude the paper by providing two opposing perspectives - for and against the
use of LLMs for automatic relevance judgments - and a compromise perspective,
informed by our analyses of the literature, our preliminary experimental
evidence, and our experience as IR researchers.
We hope to start a constructive discussion within the community to avoid a
stale-mate during review, where work is dammed if is uses LLMs for evaluation
and dammed if it doesn't
A distributional investigation of German verbs
Diese Dissertation bietet eine empirische Untersuchung deutscher Verben auf der Grundlage statistischer Beschreibungen, die aus einem großen deutschen Textkorpus gewonnen wurden. In einem kurzen Überblick über linguistische Theorien zur lexikalischen Semantik von Verben skizziere ich die Idee, dass die Verbbedeutung wesentlich von seiner Argumentstruktur (der Anzahl und Art der Argumente, die zusammen mit dem Verb auftreten) und seiner Aspektstruktur (Eigenschaften, die den zeitlichen Ablauf des vom Verb denotierten Ereignisses bestimmen) abhängt. Anschließend erstelle ich statistische Beschreibungen von Verben, die auf diesen beiden unterschiedlichen Bedeutungsfacetten basieren. Insbesondere untersuche ich verbale Subkategorisierung, Selektionspräferenzen und Aspekt. Alle diese Modellierungsstrategien werden anhand einer gemeinsamen Aufgabe, der Verbklassifikation, bewertet. Ich zeige, dass im Rahmen von maschinellem Lernen erworbene Merkmale, die verbale lexikalische Aspekte erfassen, für eine Anwendung von Vorteil sind, die Argumentstrukturen betrifft, nämlich semantische Rollenkennzeichnung. Darüber hinaus zeige ich, dass Merkmale, die die verbale Argumentstruktur erfassen, bei der Aufgabe, ein Verb nach seiner Aspektklasse zu klassifizieren, gut funktionieren. Diese Ergebnisse bestätigen, dass diese beiden Facetten der Verbbedeutung auf grundsätzliche Weise zusammenhängen.This dissertation provides an empirical investigation of German verbs conducted on the basis of statistical descriptions acquired from a large corpus of German text. In a brief overview of the linguistic theory pertaining to the lexical semantics of verbs, I outline the idea that verb meaning is composed of argument structure (the number and types of arguments that co-occur with a verb) and aspectual structure (properties describing the temporal progression of an event referenced by the verb). I then produce statistical descriptions of verbs according to these two distinct facets of meaning: In particular, I examine verbal subcategorisation, selectional preferences, and aspectual type. All three of these modelling strategies are evaluated on a common task, automatic verb classification. I demonstrate that automatically acquired features capturing verbal lexical aspect are beneficial for an application that concerns argument structure, namely semantic role labelling. Furthermore, I demonstrate that features capturing verbal argument structure perform well on the task of classifying a verb for its aspectual type. These findings suggest that these two facets of verb meaning are related in an underlying way
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences
Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin
Efficient Neural Methods for Coreference Resolution
Coreference resolution is a core task in natural language processing and in creating language technologies. Neural methods and models for automatically resolving references have emerged and developed over the last several years. This progress is largely marked by continuous improvements on a single dataset and metric. In this thesis, the assumptions that underlie these improvements are shown to be unrealistic for real-world use due to the computational and data tradeoffs made to achieve apparently high performance. The thesis outlines and proposes solutions to three issues. First, to address the growing memory requirements and restrictions on input document length, a novel, constant memory neural model for coreference resolution is proposed and shown to attain performance comparable to contemporary models. Second, to address the failure of these models to generalize across datasets, continued training is evaluated and shown to be successful for transferring coreference resolution models between domains and languages. Finally, to combat the gains obtained via the use of increasingly large pretrained language models, multitask model pruning can be applied to maintain a single (small) model for multiple datasets. These methods reduce the computational cost of running a model and the annotation cost of creating a model for any arbitrary dataset. As real-world applications continue to demand resolution of coreference, methods that reduce the technical cost of training new models and making predictions are greatly desired, which this thesis addresses
Automatic extraction of robotic surgery actions from text and kinematic data
The latest generation of robotic systems is becoming increasingly autonomous due to technological advancements and artificial intelligence. The medical field, particularly surgery, is also interested in these technologies because automation would benefit surgeons and patients. While the research community is active in this direction, commercial surgical robots do not currently operate autonomously due to the risks involved in dealing with human patients: it is still considered safer to rely on human surgeons' intelligence for decision-making issues. This means that robots must possess human-like intelligence, including various reasoning capabilities and extensive knowledge, to become more autonomous and credible. As demonstrated by current research in the field, indeed, one of the most critical aspects in developing autonomous systems is the acquisition and management of knowledge. In particular, a surgical robot must base its actions on solid procedural surgical knowledge to operate autonomously, safely, and expertly. This thesis investigates different possibilities for automatically extracting and managing knowledge from text and kinematic data. In the first part, we investigated the possibility of extracting procedural surgical knowledge from real intervention descriptions available in textbooks and academic papers on the robotic-surgical domains, by exploiting Transformer-based pre-trained language models. In particular, we released SurgicBERTa, a RoBERTa-based pre-trained language model for surgical literature understanding. It has been used to detect procedural sentences in books and extract procedural elements from them. Then, with some use cases, we explored the possibilities of translating written instructions into logical rules usable for robotic planning. Since not all the knowledge required for automatizing a procedure is written in texts, we introduce the concept of surgical commonsense, showing how it relates to different autonomy levels. In the second part of the thesis, we analyzed surgical procedures from a lower granularity level, showing how each surgical gesture is associated with a given combination of kinematic data
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