Probabilistic Models

This paper* describes a .al, ural language p~rsi.g algorithm &)r un- resu'icl,ed I,ext which uses a probabilhq-hased scoring funct,iow I,o se- le(:l, I,he "besC: parse of ~/ sent,enos acc:ording t,c~ …...

Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which assigns the most likely tag to each word in a given sentence, is one. of tire problems …...

Probabilistic models have been effective in re- solving prepositional phrase attachment am- biguity, but sparse data remains a significant problem. We propose a solution based on similarity-based smoothing, where the …...

In the task of semantic category labeling, given a text, every word in it has to be assigned a semantic category. Our language of interest is Hindi. We use the …...

This paper discusses some of the ways that the “statistical revolution” has changed and continues to change the relationship between linguistics and computational lin- guistics. I claim that it is …...

An important part of a dialogue system is the correct labelling of turns with dialogue-related meaning. This meaning is usually represented by dialogue acts, which give the system seman- tic …...

Probabilistic models of sentence com- prehension are increasingly relevant to questions concerning human language processing. However, such models are of- ten limited to syntactic factors. This paper introduces a novel …...

We extract new terminology from a text by term validation in a dictionary. Our ap- proach is based on estimating probabili- ties for previously unseen terms, i.e. not present in …...

We investigate the semantic relationship be- tween a noun and its adjectival modifiers. We introduce a class of probabilistic mod- els that enable us to to simultaneously cap- ture both …...

For annotation tasks involving independent judgments, probabilistic models have been used to infer ground truth labels from data where a crowd of many annotators labels the same items. Such models …...

A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in, e.g., Bicknell et al. (2010), event-level knowl- edge does affect …...

AbstractProbabilistic models are a useful means for analyzing large text corpora. Integrating such models with human interaction enables many new use cases. However, adding human interaction to probabilistic models requires …...

We introduce several probabilistic models for learning the lexicon of a semantic parser. Lex- icon learning is the first step of training a se- mantic parser for a new application …...

AbstractThe information revolution brought with it information pollution. Information retrieval and extraction help us cope with abundant information from diverse sources. But some sources are of anonymous authorship, and some …...

AbstractSemantic representations in the form of directed acyclic graphs (DAGs) have been introduced in recent years, and to model them, we need probabilistic models of DAGs. One model that has …...

AbstractTopic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure. Although probabilistic models can generate topic …...