Index of papers in Proc. ACL 2009 that mention
  • probabilistic model
Ai, Hua and Litman, Diane
Abstract
Since most current user simulations deploy probability models to mimic human user behaviors, how to set up user action probabilities in these models is a key problem to solve.
Evaluation Measures
However, since our simulation model is a probabilistic model , the model will take an action stochastically after the same tutor turn.
Introduction
most of these current user simulation techniques use probabilistic models to generate user actions, how to set up the probabilities in the simulations is another important problem to solve.
Introduction
For the trained user simulations, we examine two sets of probabilities trained from user corpora of different sizes, since the amount of training data will impact the quality of the trained probability models .
Related Work
Most current simulation models are probabilistic models in which the models simulate user actions based on dialog context features (Schatzmann et al., 2006).
Related Work
They first cluster dialog contexts based on selected features and then build conditional probability models for each cluster.
Related Work
In our study, we build a conditional probability model which will be described in detail in Section 3.2.1.
probabilistic model is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Das, Dipanjan and Smith, Noah A.
Conclusion
In this paper, we have presented a probabilistic model of paraphrase incorporating syntax, lexical semantics, and hidden loose alignments between two sentences’ trees.
Experimental Evaluation
It is quite promising that a linguistically-motivated probabilistic model comes so close to a string-similarity baseline, without incorporating string-local phrases.
Introduction
This syntactic framework represents a major departure from useful and popular surface similarity features, and the latter are difficult to incorporate into our probabilistic model .
Introduction
We introduce our probabilistic model in §2.
Probabilistic Model
For the present, consider it a specially-defined probabilistic model that generates sentences with a specific property, like “paraphrases s,” when 0 = p.) Given 5, Ge generates the other sentence in the pair, 5’ .
QG for Paraphrase Modeling
It is never used for parsing or for generation; it is only used as a component in the generative probability model presented in §2 (Eq.
probabilistic model is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Yang, Qiang and Chen, Yuqiang and Xue, Gui-Rong and Dai, Wenyuan and Yu, Yong
Image Clustering with Annotated Auxiliary Data
So we design a new PLSA model by joining the probabilistic model in Equation (1) and the probabilistic model in Equation (4) into a unified model, as shown in Figure 3.
Related Works
Probabilistic latent semantic analysis (PLSA) is a widely used probabilistic model (Hofmann, 1999), and could be considered as a probabilistic implementation of latent semantic analysis (L SA) (Deerwester et al., 1990).
Related Works
An extension to PLSA was proposed in (Cohn and Hofmann, 2000), which incorporated the hyperlink connectivity in the PLSA model by using a joint probabilistic model for connectivity and content.
probabilistic model is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: