CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

The Computational Linguistics Feedback Forum (CLIFF) is a group of students and faculty who gather once a week to discuss the members\u27 current research. As the word feedback suggests, the group\u27s purpose is the sharing of ideas. The group also promotes interdisciplinary contacts between researchers who share an interest in Cognitive Science. There is no single theme describing the research in Natural Language Processing at Penn. There is work done in CCG, Tree adjoining grammars, intonation, statistical methods, plan inference, instruction understanding, incremental interpretation, language acquisition, syntactic parsing, causal reasoning, free word order languages, ... and many other areas. With this in mind, rather than trying to summarize the varied work currently underway here at Penn, we suggest reading the following abstracts to see how the students and faculty themselves describe their work. Their abstracts illustrate the diversity of interests among the researchers, explain the areas of common interest, and describe some very interesting work in Cognitive Science. This report is a collection of abstracts from both faculty and graduate students in Computer Science, Psychology and Linguistics. We pride ourselves on the close working relations between these groups, as we believe that the communication among the different departments and the ongoing inter-departmental research not only improves the quality of our work, but makes much of that work possible

Extracting Computational Logic from Legal Text:A Decision Support Approach for Public Sector Automation

This research presents first steps towards a generic approach to automatically translating legal text into machine-executable computational logic. We demonstrate how this approach can be used to automate public sector processes. Since automation of legal processes is a high-risk application of AI, we use explainable AI based on natural language processing using scope-restricted pattern matching and grammatical parsing. Our approach consists of document structure inference from the raw legal text, semantically neutral pre-processing, recognition of internal and external references, target resolution for internal references, paragraph contextualization and, finally, rule extraction. Extracted rules are converted to Prolog predicates and visualized as textual lists and graphical decision trees. Our developed Law as Code prototype has been evaluated as a proof-of-concept at the Austrian Ministry of Finance and successfully demonstrated the automatic extraction of explainable rules from the Austrian Study Funding Act. This validates our approach and suggests promising future research directions, most notably the prospect of integrating GenAI Large Language Models (LLMs) into the rule extraction process, while retaining provenance and explainability

Levels and empty categories in a principles and parameters approach to parsing

Publisher final version (usually the publisher pdf

Proceedings of the Workshop on the lambda-Prolog Programming Language

The expressiveness of logic programs can be greatly increased over first-order Horn clauses through a stronger emphasis on logical connectives and by admitting various forms of higher-order quantification. The logic of hereditary Harrop formulas and the notion of uniform proof have been developed to provide a foundation for more expressive logic programming languages. The λ-Prolog language is actively being developed on top of these foundational considerations. The rich logical foundations of λ-Prolog provides it with declarative approaches to modular programming, hypothetical reasoning, higher-order programming, polymorphic typing, and meta-programming. These aspects of λ-Prolog have made it valuable as a higher-level language for the specification and implementation of programs in numerous areas, including natural language, automated reasoning, program transformation, and databases

Parsing Strategies With \u27Lexicalized\u27 Grammars: Application to Tree Adjoining Grammars

In this paper, we present a parsing strategy that arose from the development of an Earley-type parsing algorithm for TAGs (Schabes and Joshi 1988) and from some recent linguistic work in TAGs (Abeillé: 1988a). In our approach, each elementary structure is systematically associated with a lexical head. These structures specify extended domains of locality (as compared to a context-free grammar) over which constraints can be stated. These constraints either hold within the elementary structure itself or specify what other structures can be composed with a given elementary structure. The \u27grammar\u27 consists of a lexicon where each lexical item is associated with a finite number of structures for which that item is the head. There are no separate grammar rules. There are, of course, \u27rules\u27 which tell us how these structures are composed. A grammar of this form will be said to be \u27lexicalized\u27. We show that in general context-free grammars cannot be \u27lexicalized\u27. We then show how a \u27lexicalized\u27 grammar naturally follows from the extended domain of locality of TAGs and examine briefly some of the linguistic implications of our approach. A general parsing strategy for \u27lexicalized\u27 grammars is discussed. In the first stage, the parser selects a set of elementary structures associated with the lexical items in the input sentence, and in the second stage the sentence is parsed with respect to this set. The strategy is independent of nature of the elementary structures in the underlying grammar. However, we focus our attention on TAGs. Since the set of trees selected at the end of the first stage is not infinite, the parser can use in principle any search strategy. Thus, in particular, a top-down strategy can be used since problems due to recursive structures are eliminated. We then explain how the Earley-type parser for TAGs can be modified to take advantage of this approach

Discourse structure analysis for requirement mining

International audienceIn this work, we first introduce two main approaches to writing requirements and then propose a method based on Natural Language Processing to improve requirement authoring and the overall coherence, cohesion and organization of requirement documents. We investigate the structure of requirement kernels, and then the discourse structure associated with those kernels. This will then enable the system to accurately extract requirements and their related contexts from texts (called requirement mining). Finally, we relate a first experimentation on requirement mining based on texts from seven companies. An evaluation that compares those results with manually annotated corpora of documents is given to conclude

Classification-based phrase structure grammar: an extended revised version of HPSG

This thesis is concerned with a presentation of Classification -based Phrase Structure Grammar (or cPSG), a grammatical theory that has grown out of extensive revisions of, and extensions to, HPSG. The fundamental difference between this theory and HPSG concerns the central role that classification plays in the grammar: the grammar classifies strings, according to their feature structure descriptions, as being of various types. Apart from the role of classification, the theory bears a close resemblance to HPSG, though it is by no means a direct translation, including numerous revisions and extensions. A central goal in the development of the theory has been its computational implementation, which is included in the thesis.The presentation may be divided into four parts. In the first, chapters 1 and 2, we present the grammatical formalism within which the theory is stated. This consists of a development of the notion of a classificatory system (chapter 1), and the incorporation of hierarchality into that notion (chapter 2).The second part concerns syntactic issues. Chapter 3 revises the HPSG treatment of specifiers, complements and adjuncts, incorporating ideas that specifiers and complements should be distinguished and presenting a treatment of adjuncts whereby the head is selected for by the adjunct. Chapter 4 presents several options for an account of unbounded dependencies. The accounts are based loosely on that of GPSG, and a reconstruction of GPSG's Foot Feature Principle is presented which does not involve a notion of default. Chapter 5 discusses coordination, employing an extension of Rounds- Kasper logic to allow a treatment of cross -categorial coordination.In the third part, chapters 6, 7 and 8, we turn to semantic issues. We begin (Chapter 6) with a discussion of Situation Theory, the background semantic theory, attempting to establish a precise and coherent version of the theory within which to work. Chapter 7 presents the bulk of the treatment of semantics, and can be seen as an extensive revision of the HPSG treatment of semantics. The aim is to provide a semantic treatment which is faithful to the version of Situation Theory presented in Chapter 6. Chapter 8 deals with quantification, discussing the nature of quantification in Situation Theory before presenting a treatment of quantification in CPSG. Some residual questions about the semantics of coordinated noun phrases are also addressed in this chapter.The final part, Chapter 9, concerns the actual computational implementation of the theory. A parsing algorithm based on hierarchical classification is presented, along with four strategies that might be adopted given that algorithm. Also discussed are some implementation details. A concluding chapter summarises the arguments of the thesis and outlines some avenues for future research