Large-scale parsing is still a complex and timeconsuming process, often so much that it is infeasible in real-world applications. The parsing system described here addresses this ...
We present CarmelTC, a novel hybrid text classification approach for automatic essay grading. Our evaluation demonstrates that the hybrid CarmelTC approach outperforms two “bag...
This paper investigates adapting a lexicalized probabilistic context-free grammar (PCFG) to a novel domain, using maximum a posteriori (MAP) estimation. The MAP framework is gener...
In this paper we investigate the use of surface text patterns for a Maximum Entropy based Question Answering (QA) system. These text patterns are collected automatically in an uns...
Deepak Ravichandran, Abraham Ittycheriah, Salim Ro...
In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing f...
Manually constructing an inventory of word senses has suffered from problems including high cost, arbitrary assignment of meaning to words, and mismatch to domains. To overcome th...
This paper presents an unsupervised method for discriminating among the senses of a given target word based on the context in which it occurs. Instances of a word that occur in si...
We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton. For our purposes, a hub is a node in a graph with in-degree greater than o...
We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within...