We present a general framework for semantic role labeling. The framework combines a machine learning technique with an integer linear programming based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role labeling. We show that full syntactic parsing information is, by far, most relevant in identifying the argument, especially, in the very first stage—the pruning stage. Surprisingly, the quality of the pruning stage cannot be solely determined based on its recall and precision. Instead, it depends on the characteristics of the output candidates that determine the difficulty of the downstream problems. Motivated by this observation, we propose an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves its performance. Our system has been evaluated in the CoNLL-2005 shared task on semantic role labeling, and achieves the highest F1 score among 19 participants.

. This paper describes an implemented algorithm for syntactic realization of a target-language sentence from an interlingual representation called Lexical Conceptual Structure (LCS). We provide a mapping between LCS thematic roles and Abstract Meaning Representation (AMR) relations; these relations serve as input to an off-the-shelf generator (Nitrogen). There are two contributions of this work: (1) the development of a thematic hierarchy that provides ordering information for realization of arguments in their surface positions; (2) the provision of a diagnostic tool for detecting inconsistencies in an existing online LCS-based lexicon that allows us to enhance principles for thematic-role assignment. 1 Introduction This paper describes an implemented algorithm for syntactic realization of a target-language sentence from an interlingual representation called Lexical Conceptual Structure (LCS). We provide a mapping between LCS thematic roles and Abstract Meaning Representation (AMR) re...

This paper describes a multi-site project to annotate six sizable bilingual parallel corpora for interlingual content. After presenting the background and objectives of the effort, we will go on to describe the data set that is being annotated, the interlingua representation language used, an interface environment that supports the annotation task and the annotation process itself. We will then present a preliminary version of our evaluation methodology and conclude with a summary of the current status of the project along with a number of issues which have arisen.

This thesis aims at an automatic acquisition of a semantic classification for verbs. As starting point, I assume that the....

This paper describes a large-scale languageindependent evaluation of the use of Thematic Hierarchies in natural language generation. We translate from a corpus of sentences reflecting the full variety of behavior of Levin-based verb classes. The corpus is used as input to a generation system that utilizes the same thematic hierarchy for realizing relative argument surface positions in two languages: English and Spanish. The output was manually evaluated by English and Spanish speakers. The contributions of this work include: (1) an improved thematic hierarchy over an earlier implementation; (2) a large-scale evaluation of the use of thematic hierarchies in two languages; (3) an implementation of a language independent module for natural language generation; and (4) the creation of a single tool for incremental development of multilingual lexicons.

An interlingua is a notation for representing the content of a text that abstracts away from the characteristics of the language itself and focuses on the meaning (semantics) alone. Interlinguas are typically used as pivot representations in machine translation, allowing the contents of a source text to be generated in many different target languages. Due to the complexities involved, few interlinguas are more than demonstration prototypes, and only one has been used in a commercial MT system. In this article we define the components of an interlingua and the principal issues faced by designers and builders of interlinguas and interlingua MT systems, illustrating with examples from operational systems and research prototypes. We discuss current efforts to annotate texts with interlingua-based information.

This paper describes a multi-site project to annotate the interlingual content of six sizable bilingual parallel corpora. The project addresses several principal problems in parallel: specification of interlingua content and notation, development of reliable annotation methods, and evaluation of annotated corpora. As a by-product, a growing corpus of annotated texts is being produced, which may eventually be useful for machine learning of semantics-based processing.

This paper focuses on the next step in the creation of a system of meaning representation and the development of semantically-annotated parallel corpora, for use in applications such as machine translation, question answering, text summarization, and information retrieval. The work described below constitutes the first effort of any kind to annotate multiple translations of foreign-language texts with interlingual content. The resulting annotated, multilingually-induced, parallel corpora will be useful as an empirical basis for a wide range of research, including the development and evaluation of interlingual NLP systems and paraphrase-extraction systems as well as a host of other research and development efforts in theoretical and applied linguistics, foreign language pedagogy, translation studies, and other related disciplines. 1

This paper focuses on the next step in the creation of a system of meaning representation and the development of semantically-annotated parallel corpora, for use in applications such as machine translation, question answering, text summarization, and information retrieval. The work described below constitutes the first effort of any kind to provide parallel corpora annotated with detailed deep semantic information. The resulting annotated, multilingual, parallel corpora will be useful as an empirical basis for a wide range of research, including the development and evaluation of interlingual NLP systems as well as a host of other research and development efforts in theoretical and applied linguistics, foreign language pedagogy, translation studies, and other related disciplines.

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