WordNet is an on-line lexical reference system whose design is inspired by current

This article shows that the Web can be employed to obtain frequencies for bigrams that are unseen in a given corpus. We describe a method for retrieving counts for adjective-noun, noun-noun, and verb-object bigrams from the Web by querying a search engine. We evaluate this method by demonstrating: (a) a high correlation between Web frequencies and corpus frequencies; (b) a reliable correlation between Web frequencies and plausibility judgments; (c) a reliable correlation between Web frequencies and frequencies recreated using class-based smoothing; (d) a good performance of Web frequencies in a pseudodisambiguation task. 1.

Automatically summarizing vast amounts of on-line quantitative data with a short natural language paragraph has a wide range of real-world applications. However, this specific task raises a number of difficult issues that are quite distinct from the generic task of language generation: conciseness, complex sentences, floating concepts, historical background, paraphrasing power and implicit content. In this thesis, I address these specific issues by proposing a new generation model in which a first pass builds a draft containing only the essential new facts to report and a second pass incrementally revises this draft to opportunistically add as many background facts as can fit within the space limit. This model requires a new type of linguistic knowledge: revision operations, which specifyies the various ways a draft can...

This paper describes surge, a syntactic realization front-end for natural language generation systems. By gradually integrating complementary aspects of various linguistic theories within the computational framework of functional unification, surge has evolved to be one of the most comprehensive grammars of English for language generation available today. It has been successfully re-used in a variety of generators, with very different architectures and application domains. 1 Introduction This paper is an overview of surge (Systemic Unification Realization Grammar of English) a syntactic realization front-end for natural language generation systems. Developed over the last seven years 1 it embeds one of the most comprehensive computational grammar of English for generation available to date. It has been successfully re-used in eight generators, that have little in common in terms of architecture. It has also been used for teaching natural language generation at several academic inst...

Statistical language learning research takes the view that many traditional natural language processing tasks can be solved by training probabilistic models of language on a sufficient volume of training data. The design of statistical language learners therefore involves answering two questions: (i) Which of the multitude of possible language models will most accurately reflect the properties necessary to a given task? (ii) What will constitute a sufficient volume of training data? Regarding the first question, though a variety of successful models have been discovered, the space of possible designs remains largely unexplored. Regarding the second, exploration of the design space has so far proceeded without an adequate answer. The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: it identifies a new class of designs by providing a novel theory of statistical natural language processing, and it presents the foundations for a predictive theory of data requirements to assist in future design explorations. The first of these contributions is called the meaning distributions theory. This theory

Semantic relationships among words and phrases are often marked by explicit syntactic or lexical clues that help recognize such relationships in texts. Within complex nominals, however, few overt clues are available. Systems that analyze such nominals must compensate for the lack of surface clues with other information. One way is to load the system with lexical semantics for nouns or adjectives. This merely shifts the problem elsewhere: how do we define the lexical se- mantics and build large semantic lexicons? Another way is to find constructions similar to a given complex nominal, for which the relationships are already known. This is the way we chose, but it too has drawbacks.

A variety of statistical methods for noun compound analysis are implemented and compared. The results support two main conclusions. First, the use of conceptual association not only enables a broad coverage, but also improves the accuracy. Second, an analysis model based on dependency grammar is substantially more accurate than one based on deepest constituents, even though the latter is more preva- lent in the literature.

Abstract. The paper introduces a method for interpreting novel noun compounds with semantic relations. The method is built around word similarity with pretagged noun compounds, based onWordNet::Similarity. Over 1,088 training instances and 1,081 test instances from the Wall Street Journal in the Penn Treebank, the proposed method was able to correctly classify 53.3 % of the test noun compounds. We also investigated the relative contribution of the modifier and the head noun in noun compounds of different semantic types. 1

This paper describes a theory that explains both the creativity and the efficiency of people's conceptual combination. In the constraint theory, conceptual combination is controlled by three constraints of diagnosticity, plausibility, and informativeness. The constraints derive from the pragmatics of communication as applied to compound phrases. The creativity of combination arises because the constraints can be satisfied in many different ways. The constraint theory yields an algorithmic model of the efficiency of combination. The C model admits the full creativity of combination and yet efficiently settles on the best interpretation for a given phrase. The constraint theory explains many empirical regularities in conceptual combination, and makes various empirically verified predictions. In computer simulations of compound phrase interpretation, the C model has produced results in general agreement with people's responses to the same phrases

This article addresses the interpretation of nominalizations, a particular class of compound nouns whose head noun is derived from a verb and whose modifier is interpreted as an argument of this verb. Any attempt to automatically interpret nominalizations needs to take into account: (a) the selectional constraints imposed by the nominalized compound head, (b) the fact that the relation of the modifier and the head noun can be ambiguous, and (c) the fact that these constraints can be easily overridden by contextual or pragmatic factors. The interpretation of nominalizations poses a further challenge for probabilistic approaches since the argument relations between a head and its modifier are not readily available in the corpus. Even an approximation that maps the compound head to its underlying verb provides insufficient evidence. We present an approach that treats the interpretation task as a disambiguation problem and show how we can “re-create” the missing distributional evidence by exploiting partial parsing, smoothing techniques, and contextual information. We combine these distinct information sources using Ripper, a system that learns sets of rules from data, and achieve an accuracy of 86.1 % (over a baseline of 61.5%) on the British National Corpus. 1.