We present a maximum entropy approach to topic sensitive language modeling. By classifying the training data into di erent parts according to topic, extracting topic sensitive unigram features, and combining these new features with conventional N-grams in language modeling, we build a topic sensitive bigram model. This model improves both perplexity and word error rate. keywords: maximum entrop...

In this paper, we propose a coherent framework for multi-machine analysis, using group clustering model, which can be utilized predictive maintenance (PdM). The benefits from the repetitive structure posed by multiple machines and enables assessment of health condition, degradation modeling comparison machines. It is based on hierarchical probabilistic denoted Gaussian topic model (GTM), where ...

Cybersecurity threats continue to increase and are impacting almost all aspects of modern life. Being aware how vulnerabilities their exploits changing gives helpful insights into combating new threats. Applying dynamic topic modeling a time-stamped cybersecurity document collection shows the significance details concepts found in them evolving. We correlate two different temporal corpora, one ...

Recently, open-domain question-answering systems have achieved tremendous progress because of developments in large language models (LLMs), and successfully been applied to (QA) systems, or Chatbots. However, there has little question answering the geographic domain. Existing research domain relies heavily on rule-based semantic parsing approaches using few data. To develop intelligent GeoQA ag...