Extending Attribute Grammars to Support Programming-in-the-Large

Attribute grammars add specification of static semantic properties to context-free grammars, which in turn describe the syntactic structure of program units. However, context-free grammars cannot express programming-in-the-large features common in modern programming languages, including unordered collections of units, included units and sharing of included units. We present extensions to context-free grammars, and corresponding extensions to attribute grammars, suitable for defining such features. We explain how batch and incremental attribute evaluation algorithms can be adapted to support these extensions, resulting in a uniform approach to intra-unit and inter-unit static semantic analysis and translation of multi-unit programs

Structure Unification Grammar: A Unifying Framework for Investigating Natural Language

This thesis presents Structure Unification Grammar and demonstrates its suitability as a framework for investigating natural language from a variety of perspectives. Structure Unification Grammar is a linguistic formalism which represents grammatical information as partial descriptions of phrase structure trees, and combines these descriptions by equating their phrase structure tree nodes. This process can be depicted by taking a set of transparencies which each contain a picture of a tree fragment, and overlaying them so they form a picture of a complete phrase structure tree. The nodes which overlap in the resulting picture are those which are equated. The flexibility with which information can be specified in the descriptions of trees and the generality of the combination operation allows a grammar writer or parser to specify exactly what is known where it is known. The specification of grammatical constraints is not restricted to any particular structural or informational domains. This property provides for a very perspicuous representation of grammatical information, and for the representations necessary for incremental parsing. The perspicuity of SUG\u27s representation is complemented by its high formal power. The formal power of SUG allows other linguistic formalisms to be expressed in it. By themselves these translations are not terribly interesting, but the perspicuity of SUG\u27s representation often allows the central insights of the other investigations to be expressed perspicuously in SUG. Through this process it is possible to unify the insights from a diverse collection of investigations within a single framework, thus furthering our understanding of natural language as a whole. This thesis gives several examples of how insights from investigations into natural language can be captured in SUG. Since these investigations come from a variety of perspectives on natural language, these examples demonstrate that SUG can be used as a unifying framework for investigating natural language

The Acquisition of Recursion: How Formalism Articulates the Child’s Path

We distinguish three kinds of recursion: Direct Recursion (which delivers a ‘conjunction’ reading), Indirect Recursion, and Generalized Transformations. The essential argument is that Direct Recursion captures the first stage of each recursive structure. Acquisition evidence will then be provided from both naturalistic data and experimentation that adjectives, possessives, verbal compounds, and sentence complements all point to con-junction as the first stage. Then it will be argued that Indirect Recursion captures the Strong Minimalist Thesis, which allows periodic Transfer and interpretation. Why is recursion delayed and not immediate? It is argued that an interpretation of Generalized Transformations in the spirit of Tree Adjoining Grammar offers a route to explanation. A labeling algorithm combines with Generalized Transformations to provide different labels for recursive structures projection. Recursion is then achieved by substitution of a recursive node for a simple node. One simple case is to substitute a Maximal Projection for a simple non-branching lexical node. A more complex case — essential to acquisition — is to substitute a category for a lexical string. Consequently, a computational ‘psychological reality’ can be attributed to explain why recursion requires an extra step for the addition of each recursive construction on the acquisition path

A Transition-Based Directed Acyclic Graph Parser for UCCA

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201

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