We found that the generative model gets confused by punctuation and tends to predict that periods at the end of sentences are the parents of words in the sentence.

We call the generic model described above “no-rules” to distinguish it from the language-specific constraints we introduce in the sequel.

We explored two parsing models: a generative model used by several authors for unsupervised induction and a discriminative model used for fully supervised training.

We also used a generative model based on dependency model with valence (Klein and Manning, 2004).

We observe that the features of our word generation model is more effective than those of the topic association model.

Among the features of the word generation model , the most improvement was achieved with BM 25, improving the MAP by 2.27%.

Since BM25 performs the best among the word generation models , its combination with other features was investigated.

3.2.3 Word Generation Model

Our word generation model p(w | d) evaluates the prominence and the discriminativeness of a word

Therefore, we estimate the word generation model with popular IR models’ the relevance scores of a document d given 212 as a query.5

To deal with the high degree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state.

We have presented a generative model of correspondences between a world state and an unsegmented stream of text.

To learn the correspondence between a text w and a world state s, we propose a generative model p(w | s) with latent variables specifying this correspondence.

We used a simple generic model of rendering string fields: Let U) be a word chosen uniformly from those in v.

To cope with these challenges, we propose a probabilistic generative model that treats text segmentation, fact identification, and alignment in a single unified framework.

We propose a generative model for expanding queries using external collections in which dependencies between queries, documents, and expansion documents are explicitly modeled.

Theoretically, the main difference between these two instantiations of our general model is that EEM3 makes much stronger simplifying indepence assumptions than EEM1.

Our aim in this paper is to define and evaluate generative models for expanding queries using external collections.

As will become clear in §4, Diaz and Metzler’s approach is an instantiation of our general model for external expansion.

We are driven by the same motivation, but where they considered rank-based result combinations and simple mixtures of query models, we take a more principled and structured approach, and develop four versions of a generative model for query expansion using external collections.