[PDF] Top 20 EM Decipherment for Large Vocabularies

... exact EM training and only n-gram language models on the target side: It has an estimated runtime of ...(using EM training and Bayesian inference) use context vectors as an additional source of information: ... See full document

... but then we must use a sparse matrix multiplica- tion as in Williams et al. (2015), which is neither as fast nor as easy to implement. A comparison be- tween these two approaches is shown in Figure 1. We find that ... See full document

... ing EM to attack decipherment ...simple decipherment models are able to crack homophonic substitution ciphers with high accuracy if a large number of random restarts are used but almost ... See full document

... We notice that all the feature-based models (both directed Feature-HMM and undi- rected log-linear models) with orthographic and length features outperformed the EM- based decipherment approach. The only ... See full document

... small vocabularies and exploited in the form of semantic grammars, unification-based grammars, case-frames or semantic nets are not viable for vocabularies of 10-20000 words or ...for large ... See full document

... on large amounts of translation pairs learned from parallel data to train their linear transforma- ...pervised decipherment, without any parallel ... See full document

... (2012) suggest to include a context vector step in between EM iterations for large vocabulary tasks. Using the Viterbi decoding of the source se- quence from the last E-step and the corpus used to train the ... See full document

... Embedding and projection matrices are com- monly used in neural language models (NLM) as well as in other sequence processing net- works that operate on large vocabularies. We examine such matrices in ... See full document

... apply EM, as pro- posed in (Knight et ...the EM algorithm has a computa- tional complexity of O(N · V 2 · R) and the com- plexity of Bayesian method is O(N · V · R), where V is the size of plaintext ... See full document

... a large part of online data involves a spatial dimension, geographic en- tities and their semantics play a central role in the LOD cloud, facilitating the geospatial grounding of scientific and commercial data ... See full document

... HMM decipherment can decipher disguised text based on the language model regardless of the encryption ...The decipherment approach we proposed can cover more disguised cases than spelling correction ... See full document

... Data and so reasoning over it has naturally been tack- led by several researchers, see for example [34], [35] and [10]. Given the divergence of linked data from the Semantic Web ideal, a wide variety of non- standard ... See full document

... It could be argued that because these systems allow free text entry they are closer to free tagging systems than the archives which, while allowing some free text entry, for the most par[r] ... See full document

... This evidence includes partially erased writing in the area under scrutiny, smudges of carbon or stains from the rubber eraser, indentations of erased strokes, disturbed paper fibers or [r] ... See full document

... In this paper we propose a method for the automatic decipherment of lost languages. Given a non-parallel corpus in a known re- lated language, our model produces both alphabetic mappings and translations of words ... See full document

... In deciding whether such maxims formed part of the coûtume as applied in Jersey, the Royal Court stated that it was important to examine their origins care- fully; and in this case, t[r] ... See full document

... better dictionaries of OOV loanwords. This result confirms that OOV borrowed words is an important type of OOVs, and with proper modeling it has the potential to improve translation by a large margin. ... See full document

... extraordinarily large feature space, and individual vectors for each sample are going to be extremely ...hibitively large or impractical for real-world ... See full document

... Information extraction from free medical text using Natural Language Processing (NLP) is currently an important field considering the huge and still growing amount of unstructured textual documents in the medical domain ... See full document

... Using our new algorithm we are able to decipher the Zodiac-408 with just a beam size of 26 and a language model order of size 8. By keeping track of the gold hypothesis while performing the beam search, we can see that ... See full document