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This paper presents a syntax-driven approach to question answering, specifically the answer-sentence selection problem for short-answer questions. Rather than using syntactic features to augment existing statistical classifiers (as in previous work), we build on the idea that questions and their (correct) answers relate to each other via loose but(More)
We present a novel classifier-based deter-ministic parser for Chinese constituency parsing. Our parser computes parse trees from bottom up in one pass, and uses classifiers to make shift-reduce decisions. Trained and evaluated on the standard training and test sets, our best model (using stacked classifiers) runs in linear time and has labeled precision and(More)
Many network attacks forge the source address in their IP packets to block traceback. Recently, research activity has focused on packet-tracing mechanisms to counter this deception. Unfortunately, these mechanisms are either too expensive or ineffective against distributed attacks where traffic comes from multiple directions, and the volume in each(More)
A range of Natural Language Processing tasks involve making judgments about the semantic relatedness of a pair of sentences , such as Recognizing Textual En-tailment (RTE) and answer selection for Question Answering (QA). A key challenge that these tasks face in common is the lack of explicit alignment annotation between a sentence pair. We capture the(More)
Many problems in NLP require solving a cascade of subtasks. Traditional pipeline approaches yield to error propagation and prohibit joint train-ing/decoding between subtasks. Existing solutions to this problem do not guarantee non-violation of hard-constraints imposed by subtasks and thus give rise to inconsistent results, especially in cases where(More)
Translated bi-texts contain complementary language cues, and previous work on Named Entity Recognition (NER) has demonstrated improvements in performance over monolingual taggers by promoting agreement of tagging decisions between the two languages. However, most previous approaches to bilingual tagging assume word alignments are given as fixed input, which(More)
Different languages contain complementary cues about entities, which can be used to improve Named Entity Recognition (NER) systems. We propose a method that formulates the problem of exploring such signals on unannotated bilingual text as a simple Integer Linear Program, which encourages entity tags to agree via bilingual constraints. Bilingual NER(More)
This paper discusses a general architecture for intelligent software agents. It can be used to construct agents that engage in high-level reasoning by employing standard reasoning engines as plug-in components, while communicating with other agents by means of the standard FIPA-based communication protocols. The approach discussed uses internal micro-agents(More)
This paper describes Stanford University's submission to the Shared Evaluation Task of WMT 2012. Our proposed metric (SPEDE) computes probabilistic edit distance as predictions of translation quality. We learn weighted edit distance in a probabilistic finite state machine (pFSM) model, where state transitions correspond to edit operations. While standard(More)