Describing São Tomense Using a Tree-Adjoining Meta-Grammar

Poster sessionInternational audienceIn this paper, we show how the interactions between the tense, aspect and mood preverbal markers in São Tomense can be formally and concisely described at an abstract level, using the concept of projection. More precisely, we show how to encode the different valid orders of preverbal markers in an abstract description of a Tree-Adjoining Grammar of São Tomense. This description is written using the XMG meta-grammar language (Crabbé and Duchier, 2004)

Modelling Discourse in STAG: Subordinate Conjunctions and Attributing Phrases

International audienceWe propose a new model in STAG syntax and semantics for subordinate conjunctions (SubConjs) and attributing phrases - attitude/reporting verbs (AVs; "believe", "say") and attributing prepositional phrase (APPs; "according to").This model is discourse-oriented, and is based on the observation that SubConjs and AVs are not homogeneous categories. Indeed, previous work has shown that SubConjs can be divided into two classes according to their syntactic and semantic properties. Similarly, AVs have two different uses in discourse: evidential and intentional. While evidential AVs and APPs have strong semantic similarities, they do not appear in the same contexts when SubConjs are at play. Our proposition aims at representing these distinctions and capturing these various discourse-related interactions

Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing

We present a novel technique to remove spurious ambiguity from transition systems for dependency parsing. Our technique chooses a canonical sequence of transition operations (computation) for a given dependency tree. Our technique can be applied to a large class of bottom-up transition systems, including for instance Nivre (2004) and Attardi (2006)

Models for Improved Tractability and Accuracy in Dependency Parsing

Automatic syntactic analysis of natural language is one of the fundamental problems in natural language processing. Dependency parses (directed trees in which edges represent the syntactic relationships between the words in a sentence) have been found to be particularly useful for machine translation, question answering, and other practical applications. For English dependency parsing, we show that models and features compatible with how conjunctions are represented in treebanks yield a parser with state-of-the-art overall accuracy and substantial improvements in the accuracy of conjunctions. For languages other than English, dependency parsing has often been formulated as either searching over trees without any crossing dependencies (projective trees) or searching over all directed spanning trees. The former sacrifices the ability to produce many natural language structures; the latter is NP-hard in the presence of features with scopes over siblings or grandparents in the tree. This thesis explores alternative ways to simultaneously produce crossing dependencies in the output and use models that parametrize over multiple edges. Gap inheritance is introduced in this thesis and quantifies the nesting of subtrees over intervals. The thesis provides O(n6) and O(n5) edge-factored parsing algorithms for two new classes of trees based on this property, and extends the latter to include grandparent factors. This thesis then defines 1-Endpoint-Crossing trees, in which for any edge that is crossed, all other edges that cross that edge share an endpoint. This property covers 95.8% or more of dependency parses across a variety of languages. A crossing-sensitive factorization introduced in this thesis generalizes a commonly used third-order factorization (capable of scoring triples of edges simultaneously). This thesis provides exact dynamic programming algorithms that find the optimal 1-Endpoint-Crossing tree under either an edge-factored model or this crossing-sensitive third-order model in O(n4) time, orders of magnitude faster than other mildly non-projective parsing algorithms and identical to the parsing time for projective trees under the third-order model. The implemented parser is significantly more accurate than the third-order projective parser under many experimental settings and significantly less accurate on none

Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines

Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF

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