The Racing Mind and the Path of Love: automatic extraction of image schematic triggers in knowledge graphs generated from natural language

Embodied Cognition and Cognitive Metaphors Theory take their origin from our use of language: sensorimotor triggers are disseminated in our daily communication, expression and commonsense knowledge. We propose, in this work, a first attempt of image-schematic triggers automatic extraction, starting from knowledge graphs automatically generated from natural language. The methodology proposed here is conceived as a modular addition integrated in the FRED tool, able to generate knowledge graphs from natural language, while it has its foundation in querying ImageSchemaNet, the Image Schematic layer developed on top of FrameNet and integrated in the Framester resource. This methodology allows the extraction of sensorimotor triggers from WordNet, VerbNet, MetaNet, BabelNet and many more

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

Automatic design of multimodal presentations

We describe our attempt to integrate multiple AI components such as planning, knowledge representation, natural language generation, and graphics generation into a functioning prototype called WIP that plans and coordinates multimodal presentations in which all material is generated by the system. WIP allows the generation of alternate presentations of the same content taking into account various contextual factors such as the user\u27s degree of expertise and preferences for a particular output medium or mode. The current prototype of WIP generates multimodal explanations and instructions for assembling, using, maintaining or repairing physical devices. This paper introduces the task, the functionality and the architecture of the WIP system. We show that in WIP the design of a multimodal document is viewed as a non-monotonic process that includes various revisions of preliminary results, massive replanning and plan repairs, and many negotiations between design and realization components in order to achieve an optimal division of work between text and graphics. We describe how the plan-based approach to presentation design can be exploited so that graphics generation influences the production of text and vice versa. Finally, we discuss the generation of cross-modal expressions that establish referential relationships between text and graphics elements

Combining Representation Learning with Logic for Language Processing

The current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed vector representations of symbols via gradient-based optimization. They require little or no hand-crafted features, thus avoiding the need for most preprocessing steps and task-specific assumptions. However, in many cases representation learning requires a large amount of annotated training data to generalize well to unseen data. Such labeled training data is provided by human annotators who often use formal logic as the language for specifying annotations. This thesis investigates different combinations of representation learning methods with logic for reducing the need for annotated training data, and for improving generalization.Comment: PhD Thesis, University College London, Submitted and accepted in 201

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail