We report on experiments in reference resolution using a decision tree approach. We started with a standard feature set used in previous work, which led to moderate results. A closer examination of the performance of the features for different forms of anaphoric expressions showed good results for pronouns, moderate results for proper names, and poor results for definite noun phrases. We then i...

The discovery of semantic relations in text plays an important role in many NLP applications. This paper presents a method for the automatic classification of semantic relations in nominalized noun phrases. Nominalizations represent a subclass of NP constructions in which either the head or the modifier noun is derived from a verb while the other noun is an argument of this verb. Especially des...

The process of conceptual combination involves accessing two or more concepts and, determining how they fit dogether to form a new concept. In a sense, conceptual combination is very broad in scope, involved in many situations in natural language understanding. For example, understanding a story probably includes the combining of concepts of the individual sentences. Understanding a sentence, i...

This paper studies entailments between sentences like three boys kissed five girls involving two or more numerically quantified noun phrases that are interpreted as expressing cumulative quantification in the sense of Scha (1984). A precise characterization of when one such sentence entails another such sentence that differs from it only in the numerals is crucially important to evaluate claims...

We describe a mechanism for automatically acquiring verb subcategorization frames and their frequencies in a large corpus. A tagged corpus is first partially parsed to identify noun phrases and then a finear grammar is used to estimate the appropriate subcategorization frame for each verb token in the corpus. In an experiment involving the identification of six fixed subcategorization frames, o...

Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases. In this paper, we propose a machine learning method that enables features over sets of noun phrases, resulting in a first-order probabilistic model for coreference. We outline a set of approximations that make this approach practical, and apply our method to the ACE coreference dataset, achi...

A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are p...

A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are p...