International Conference on Semantic Computing 

About: International Conference on Semantic Computing is an academic conference. The conference publishes majorly in the area(s): The Internet & Communications satellite. Over the lifetime, 589 publications have been published by the conference receiving 4338 citations.

Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity

Abstract: This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.

OntoNotes: A Unified Relational Semantic Representation

Abstract: The OntoNotes project is creating a corpus of large-scale, accurate, and integrated annotation of multiple levels of the shallow semantic structure in text. Such rich, integrated annotation covering many levels will allow for richer, cross-level models enabling significantly better automatic semantic analysis. At the same time, it demands a robust, efficient, scalable mechanism for storing and accessing these complex inter-dependent annotations. We describe a relational database representation that captures both the inter- and intra-layer dependencies and provide details of an object-oriented API for efficient, multi-tiered access to this data.

Lexical and Discourse Analysis of Online Chat Dialog

Abstract: One of the ultimate goals of natural language processing (NLP) systems is understanding the meaning of what is being transmitted, irrespective of the medium (e.g., written versus spoken) or the form (e.g., static documents versus dynamic dialogues). Although much work has been done in traditional language domains such as speech and static written text, little has yet been done in the newer communication domains enabled by the Internet, e.g., online chat and instant messaging. This is in part due to the fact that there are no annotated chat corpora available to the broader research community. The purpose of this research is to build a chat corpus, tagged with lexical (token part-of-speech labels), syntactic (post parse tree), and discourse (post classification) information. Such a corpus can then be used to develop more complex, statistical-based NLP applications that perform tasks such as author profiling, entity identification, and social network analysis.

Unrestricted Coreference: Identifying Entities and Events in OntoNotes

Abstract: Most research in the field of anaphora or coreference detection has been limited to noun phrase coreference, usually on a restricted set of entities, such as ACE entities. In part, this has been due to the lack of corpus resources tagged with general anaphoric coreference. The OntoNotes project is creating a large-scale, accurate corpus for general anaphoric coreference that covers entities and events not limited to noun phrases or a limited set of entity types. The coreference layer in OntoNotes constitutes one part of a multi-layer, integrated annotation of shallow semantic structure in text. This paper presents an initial model for unrestricted coreference based on this data that uses a machine learning architecture with state-of-the-art features. Significant improvements can be expected from using such cross-layer information for training predictive models. This paper describes the coreference annotation in OntoNotes, presents the baseline model, and provides an analysis of the contribution of this new resource in the context of recent MUC and ACE results.