Message-Passing Protocols for Real-World Parsing -- An Object-Oriented Model and its Preliminary Evaluation

We argue for a performance-based design of natural language grammars and their associated parsers in order to meet the constraints imposed by real-world NLP. Our approach incorporates declarative and procedural knowledge about language and language use within an object-oriented specification framework. We discuss several message-passing protocols for parsing and provide reasons for sacrificing completeness of the parse in favor of efficiency based on a preliminary empirical evaluation.Comment: 12 pages, uses epsfig.st

Concurrent Lexicalized Dependency Parsing: The ParseTalk Model

A grammar model for concurrent, object-oriented natural language parsing is introduced. Complete lexical distribution of grammatical knowledge is achieved building upon the head-oriented notions of valency and dependency, while inheritance mechanisms are used to capture lexical generalizations. The underlying concurrent computation model relies upon the actor paradigm. We consider message passing protocols for establishing dependency relations and ambiguity handling.Comment: 90kB, 7pages Postscrip

Incremental Centering and Center Ambiguity

In this paper, we present a model of anaphor resolution within the framework of the centering model. The consideration of an incremental processing mode introduces the need to manage structural ambiguity at the center level. Hence, the centering framework is further refined to account for local and global parsing ambiguities which propagate up to the level of center representations, yielding moderately adapted data structures for the centering algorithm.Comment: 6 pages, uuencoded gzipped PS file (see also Technical Report at: http://www.coling.uni-freiburg.de/public/papers/cogsci96-center.ps.gz

A Conceptual Reasoning Approach to Textual Ellipsis

We present a hybrid text understanding methodology for the resolution of textual ellipsis. It integrates conceptual criteria (based on the well-formedness and conceptual strength of role chains in a terminological knowledge base) and functional constraints reflecting the utterances' information structure (based on the distinction between context-bound and unbound discourse elements). The methodological framework for text ellipsis resolution is the centering model that has been adapted to these constraints.Comment: 5 pages, uuencoded gzipped PS file (see also Technical Report at: http://www.coling.uni-freiburg.de/public/papers/ecai96.ps.gz

Anaphor resolution and the scope of syntactic constraints

An anaphor resolution algorithm is presented which relies on a combination of strategies for narrowing down and selecting from antecedent sets for re exive pronouns, nonre exive pronouns, and common nouns. The work focuses on syntactic restrictions which are derived from Chomsky's Binding Theory. It is discussed how these constraints can be incorporated adequately in an anaphor resolution algorithm. Moreover, by showing that pragmatic inferences may be necessary, the limits of syntactic restrictions are elucidated

Augmented trading:From news articles to stock price predictions using semantics

This thesis tries to answer the question how to predict the reaction of the stock market to news articles using the latest suitable developments in Natural Language Processing. This is done using text classiffication where a new article is matched to a category of articles which have a certain influence on the stock price. The thesis first discusses why analysis of news articles is a feasible approach to predicting the stock market and why analysis of past prices should not be build upon. From related work in this domain two main design choices are extracted; what to take as features for news articles and how to couple them with the changes in stock price. This thesis then suggests which different features are possible to extract from articles resulting in a template for features which can deal with negation, favorability, abstracts from companies and uses domain knowledge and synonyms for generalization. To couple the features to changes in stock price a survey is given of several text classiffication techniques from which it is concluded that Support Vector Machines are very suitable for the domain of stock prices and extensive features. The system has been tested with a unique data set of news articles for which results are reported that are signifficantly better than random. The results improve even more when only headlines of news articles are taken into account. Because the system is only tested with closing prices it cannot concluded that it will work in practice but this can be easily tested if stock prices during the days are available. The main suggestions for feature work are to test the system with this data and to improve the filling of the template so it can also be used in other areas of favorability analysis or maybe even to extract interesting information out of texts