Martha Palmer

VERB SEMANTICS AND LEXICAL SELECTION

This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.

The Proposition Bank: An Annotated Corpus of Semantic Roles

The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated. We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus. We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty trace categories of the treebank.

OntoNotes: The 90% Solution

We describe the OntoNotes methodology and its result, a large multilingual richly-annotated corpus constructed at 90% interannotator agreement. An initial portion (300K words of English newswire and 250K words of Chinese newswire) will be made available to the community during 2007.

The Penn Chinese TreeBank: Phrase structure annotation of a large corpus

With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality.

SemEval-2007 Task-17: English Lexical Sample, SRL and All Words

This paper describes our experience in preparing the data and evaluating the results for three subtasks of SemEval-2007 Task-17 - Lexical Sample, Semantic Role Labeling (SRL) and All-Words respectively. We tabulate and analyze the results of participating systems.

Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars

Syntax-based statistical machine translation (MT) aims at applying statistical models to structured data. In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. We first introduce our approach to inducing such a grammar from parallel corpora. Second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. We introduce a polynomial time decoding algorithm for the model. We evaluate the outputs of our MT system using the NIST and Bleu automatic MT evaluation software. The result shows that our system outperforms the baseline system based on the IBM models in both translation speed and quality.

Building a large-scale annotated Chinese corpus

In this paper we address issues related to building a large-scale Chinese corpus. We try to answer four questions: (i) how to speed up annotation, (ii) how to maintain high annotation quality, (iii) for what purposes is the corpus applicable, and finally (iv) what future work we anticipate.

Animation control for real-time virtual humans

The computation speed and control methods needed to portray 3D virtual humans suitable for interactive applications have improved dramatically in recent years. Real-time virtual humans show increasingly complex features along the dimensions of appearance, function, time, autonomy, and individuality. The virtual human architecture we’ve been developing at the University of Pennsylvania is representative of an emerging generation of such architectures and includes low-level motor skills, a mid-level parallel automata controller, and a high-level conceptual representation for driving virtual humans through complex tasks. The architecture—called Jack— provides a level of abstraction generic enough to encompass natural-language instruction representation as well as direct links from those instructions to animation control.

Semantic Role Labeling

A basic aim of computational linguistics (CL) is the study and design of computational models of natural language semantics. Although frequency-based approaches—for example, distributional semantics—provide effective and concrete solutions for natural language applications, they still fail to fully reconcile the field with the theoreticallinguistic soul. In contrast, semantic role labeling (SRL), a recent new area of CL, aims to automatically provide (shallow) semantic layers using modern linguistic theories of semantic roles, also exploitable by language applications. The centrality and importance of such theories in CL has promoted the development of a rather large body of work on SRL; its many aspects and research directions make it difficult to survey the field. Palmer, Gildea, and Xue’s book provides an excellent description of such work, detailing all its main concepts and practical aspects. The authors accurately illustrate all important ingredients to acquire a global and precise view of the field, namely, (i) the theoretical framework, ranging from linking theory to theta roles, Levin’s classes and frame semantics; (ii) computational models based on syntactic representations derived from diverse parsing paradigms; (iii) several resources in different languages; (iv) many machine learning approaches and strategies; and (v) portability to other languages and domains. This book is mainly directed to practitioners who want to contribute to SRL or who want to simply use its technology in natural language applications. As an “Ariadne’s ball of thread,” this book will guide the reader through the conceptual SRL labyrinth, saving months of work needed to understand theory and practice of this exciting research field. The book is divided into four content chapters.

Dynamically altering agent behaviors using natural language instructions

Smart avatars are virtual human representations controlled by real people. Given instructions interactively, smart avatars can act as autonomous or reactive agents. During a real-time simulation, a user should be able to dynamically refine his or her avatar’s behavior in reaction to simulated stimuli without having to undertake a lengthy off-line programming session. In this paper, we introduce an architecture, which allows users to input immediate or persistent instructions using natural language and see the agents’ resulting behavioral changes in the graphical output of the simulation.