The first formally elaborated theory of a generative approach to word meaning, The Generative Lexicon lays the foundation for an implemented computational treatment of word meaning that connects explicitly to a compositional semantics. The Generative Lexicon presents a novel and exciting theory of lexical semantics that addresses the problem of the "multiplicity of word meaning"; that is, how we are able to give an infinite number of senses to words with finite means. The first formally elaborated theory of a generative approach to word meaning, it lays the foundation for an implemented computational treatment of word meaning that connects explicitly to a compositional semantics. In contrast to the static view of word meaning (where each word is characterized by a predetermined number of word senses) that imposes a tremendous bottleneck on the performance capability of any natural language processing system, Pustejovsky proposes that the lexicon becomes an active—and central—component in the linguistic description. The essence of his theory is that the lexicon functions generatively, first by providing a rich and expressive vocabulary for characterizing lexical information; then, by developing a framework for manipulating fine-grained distinctions in word descriptions; and finally, by formalizing a set of mechanisms for specialized composition of aspects of such descriptions of words, as they occur in context, extended and novel senses are generated. The subjects covered include semantics of nominals (figure/ground nominals, relational nominals, and other event nominals); the semantics of causation (in particular, how causation is lexicalized in language, including causative/unaccusatives, aspectual predicates, experiencer predicates, and modal causatives); how semantic types constrain syntactic expression (such as the behavior of type shifting and type coercion operations); a formal treatment of event semantics with subevents); and a general treatment of the problem of polysemy. Language, Speech, and Communication series Bradford Books imprint

No abstract

Recent work in computational linguistics points out the need for systems to be sensitive to the veracity or factuality of events as mentioned in text; that is, to recognize whether events are presented as corresponding to actual situations in the world, situations that have not happened, or situations of uncertain interpretation. Event factuality is an important aspect of the representation of events in discourse, but the annotation of such information poses a representational challenge, largely because factuality is expressed through the interaction of numerous linguistic markers and constructions. Many of these markers are already encoded in existing corpora, albeit in a somewhat fragmented way. In this article, we present FACTBANK, a corpus annotated with information concerning the factuality of events. Its annotation has been carried out from a descriptive framework of factuality grounded on both theoretical findings and data analysis. FactBank is built on top of TimeBank, adding to it an additional level of semantic information.

This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an oversampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions.

This study proposes a system to automatically analyze clinical temporal events in a fine-grained level in SemEval-2017. Support vector machine (SVM) and conditional random field (CRF) were implemented in our system for different subtasks, including detecting clinical relevant events and time expression, determining their attributes , and identifying their relations with each other within the document. Domain adaptation was the main challenge this year. Unified Medical Language System was consulted to generalize events specific to each domain. The results showed our system's capability of domain adaptation.