Alternating Roots in Lummi

Chapter 2 - Categorial Type Logics

Extracting perceived landscape properties from text sources

Natural Language Processing focuses on the interaction between computers and the natural language used by humans. In other words, the computer process extracts information from input in the form of natural language and / or produces output which is also in the form of natural language.Newspapers are printed sheets containing reports that occur in the community. However, the titles of newspapers often have an ambiguous meaning. When a written or spoken word, phrase, sentence, or even paragraph contains ambiguity, it is automatically ineffective and communicative for the reader or listener. For this reason, it is necessary to identify sentences to make it easier for readers to understand the meaning of an ambiguous title.To make it easier to identify ambiguous sentences in newspapers requires a shift Reduce parsing algorithm, using the Shift Reduce parsing algorithm can be useful to find out what the news writer wants to convey.

With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language. This knowledge can increase their behavioral awareness and allow them to take action to meet their health goals. It can also bridge the gap between the vast collection of personal health data and the summary generation required to describe an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal(Weber and Achananuparp 2016) and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.

Philosophical Speculation and Cognitive Science Comments of William Lycan’s Logical Form in Natural Language

As a result of the recent information boom, the contents of the Internet are increasing in the multilingual form and mostly in the form of natural languages. Therefore, the research on Natural Language Processing (NLP) tasks of regional languages is very important. Dogri, the state language of Jammu and Kashmir (J&K), has remained as a low-resourced language in the field of NLP. In this survey, the contributions of various researchers for Dogri NLP are reviewed and discussed. Sources of Dogri dataset and tools for processing Dogri are explored. Challenges such as the complex linguistic tasks and lack of digital resources are also discussed. The future scope and basic requirements for improving various Dogri language processing tasks are highlighted. KeywordsDogriNatural language processingLow resource languageDigital resources

1. Preface 2. Japanese Modals are Conditionals (by Akatsuka, Noriko) 3. The Cairene Arabic Verb Without Form Classes (by Darden, Bill) 4. The Transition from Oral to Written Competence: Evidence from Teaching Freshman Composition (by Deane, Paul D.) 5. Two Types of World-Greating Predicates (by Farkas, Donka F.) 6. Tojolabal (Mayan) Kinterms and the Theory of Markedness or a Near Triumph of the Feminie Gender (by Furbee, N. Louanna) 7. Tone and Accent in Llogoori (by Goldsmith, John A.) 8. Purpose Infinitives and Their Relatives (by Green, Georgia M.) 9. A Semantic Etymology 10. Are Conditionals Topics? The Japanese Case (by Jacobsen, Wesley M.) 11. Gender and Sex in Standard Modern greek Pet Names (by Kazazis, Kostas) 12. Philosophical Speculation and Cognitive Science Comments of William Lycan's Logical Form in Natural Language (by Lakoff, George) 13. Symmetric Relations (by Langendoen, D. Terence) 14. The Korean Precursors of Generative Phonology (by Manaster-Ramer, Alexis) 15. Why Grammars are Not Monolithic (by Mufwene, Salikoko S.) 16. On Extracting from Asymmetrical Structures (by Na, Younghee) 17. The Markedness of Plurality (by Ojeda, Almerindo E.) 18. Phantom Succesors and the French FAIRE PAR Connstruction (by Postal, Paul M.) 19. A Paper on Yiddish for James D. McCawley (by Sadock, Jerrold M.) 20. Survival of the Positive: History of Japanese Antonyms (by Tagashira, Y.) 21. Lexical Phonology and Japanese Vowel Devoicing (by Vance, Timothy J.) 22. First He Called Her a Philologist and Then She Insulted Him (by Winters, Margaret E.) 23. Jottings on Adpositions, Case Inflections, Government, and Agreement (by Zwicky, Arnold M.) 24. Indices

Chapter 12 - Making Sense of Unstructured Natural Language Information

Enabling computer systems to respond to conversational human language is a challenging problem with wide-ranging applications in the field of robotics and human computer interaction. Specifically, in image searches, humans tend to describe objects in fine-grained detail like color or company, for which conventional retrieval algorithms have shown poor performance. In this paper, a novel approach for open vocabulary image retrieval, capable of selecting the correct candidate image from among a set of distractions given a query in natural language form, is presented. Our methodology focuses on generating a robust set of image-text projections capable of accurately representing any image, with an objective of achieving high recall. To this end, an ensemble of classifiers is trained on ImageNet for representing high-resolution objects, Cifar 100 for smaller resolution images of objects and Caltech 256 for challenging views of everyday objects, for generating category-based projections. In addition to category based projections, we also make use of an image captioning model trained on MS COCO and Google Image Search (GISS) to capture additional semantic/latent information about the candidate images. To facilitate image retrieval, the natural language query and projection results are converted to a common vector representation using word embeddings, with which query-image similarity is computed. The proposed model when benchmarked on the RefCoco dataset, achieved an accuracy of 68.8%, while retrieving semantically meaningful candidate images.

In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language. The attendees of the tutorial will be able to take away from this tutorial, (1) the basic ideas around how modern NLP and NLG techniques could be applied to describe and summarize textual data in format that is non-linguistic in nature or has some structure, and (2) a few interesting open-ended questions, which could lead to significant research contributions in future. The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. Various solutions, starting from traditional rule based/heuristic driven and modern data-driven and ultra-modern deep-neural style architectures will be discussed, followed by a brief discussion on evaluation and quality estimation. A significant portion of the tutorial will be dedicated towards unsupervised, scalable, and adaptable solutions, given that systems for such an important task will never naturally enjoy sustainable large scale domain independent labeled (parallel) data.

There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this article presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. We developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports.

From API to NLI: A new interface for library reuse

Summary form only given. As the fear of semantics is declining, let's formalize the unformalizable! The address is focusing on the feasibility and necessity of accessing directly the comprehensive meaning of natural language texts, data, images, etc., to emulate human understanding by the computer. It is based on the premise that, without such understanding, no real-life application can reach the precision that human users of computational systems require. Moreover, meaning access and processing goes well beyond natural language applications, even as the scope of those keeps growing. The main obstacle for this seemingly reasonable goal is the widespread prejudice against meaning as unknowable in principle because it is “subjective,” context-sensitive, complex, messy, etc. This has not been our experience, and it is worth talking, both practically and theoretically, about the know-how that a research group should acquire to approach the processing tasks semantically. A convincing case can be made for the feasibility and affordability of the approach as well, especially in the light of our planning to make our resources Open Source, a plan that may be followed by other computational semanticists. It is hoped, therefore, that this address will further reduce the fear of, and prejudice against semantics. It will focus on our Ontological Semantic Technology, a mature formal theory of meaning that has long overcome the crippling fixation on first-order logic as the only possible formalism for everything. This fixation has prevented generations of researchers to access natural language meaning to the grain size that makes meaning representation usable, both theoretically and in practical applications. A small set of predicates using a smaller number of variables as arguments may work well for formal languages but natural language defeats such an approach. The fear of semantics is based on the faulty conclusion that, therefore, natural language meaning is not formalizable. Throw away an insufficient method, and the task becomes feasible!

This article discusses what kind of entity the proposed semantic Web is, principally by reference to the relationship of natural language structure to knowledge representation. The concept of the SW has two distinct origins, and this, bifurcation persists in two differing lines of SW research: one closely allied to notions of documents and natural language processing (NLP) and one not. These differences of emphasis or content carry with them different commitments about what it is to interpret a KR and what the interpretation method has to do with meaning in natural language.

The year is 2030. A two-young-child, two-parent household, the Rubensteins, owns and employs a state-of-the-art household robot, Rodney. With the parents out, the children ask Rodney to perform some action \(\alpha \) that violates a Rubensteinian ethical principle \(P_R\). Rodney replies: (\(s_1\)) “Doing that would be (morally) wrong, kids.” The argument the children give Rodney in protest is that another household, the Müllers, also has a robot, Ralph; and the kids argue that he routinely performs \(\alpha \). As a matter of fact, Ralph’s doing \(\alpha \) violates no Müllerian ethical principle \(P_M\). Ralph’s response to the very same request from the children he tends is: (\(s_2\)) “Okay, doing that is (morally) fine, kids.” What is the meaning of the utterances made by Rodney and Ralph? We answer this question by presenting and employing a novel, formal, inferential theory of meaning in natural language: hypergraphical inferential semantics (\(\mathscr {H}\!\! \mathscr {I}\!\! \mathscr {S}\)), which is in the general spirit of proof-theoretic semantics, which is in turn antithetical to Montagovian model-theoretic semantics. \(\mathscr {H}\!\! \mathscr {I}\!\! \mathscr {S}\), applied even to sentences logically simpler than \(s_1\) and \(s_2\), implies that human-level natural language understanding (NLU) is Turing-uncomputable.