In this paper, we will present an efficient method to compute the co-occurrence counts of any pair of substring in a parallel corpus, and an algorithm that make use of these counts to create subsentential alignments on such a corpus. This algorithm has the advantage of being as general as possible regarding the segmentation of text.
This paper investigates the use of machine learning algorithms to label modifier-noun compounds with a semantic relation. The attributes used as input to the learning algorithms are the web frequencies for phrases containing the modifier, noun, and a prepositional joining term. We compare and evaluate different algorithms and different joining phrases on Nastase and Szpakowicz’s (2003) dataset of 600 modifier-noun compounds.
Recent text and speech processing applications such as speech mining raise new and more general problems related to the construction of language models. We present and describe in detail several new and efﬁcient algorithms to address these more general problems and report experimental results demonstrating their usefulness.
Lecture Algorithm design - Chapter 5: Divide and conquer I include all of the following: Mergesort, counting inversions, closest pair of points, randomized quicksort, median and selection. For more details, inviting you refer to the above lesson.
In this topic, we will look at: Justification for analysis, quadratic and polynomial growth, counting machine instructions, Landau symbols, Big-Q as an equivalence relation, little-o as a weak ordering.
During the last three decades, public academic research in cryptography has exploded.
While classical cryptography has been long used by ordinary people, computer
cryptography was the exclusive domain of the world’s militaries since the World War
II. Today, state-of the-art computer cryptography is practiced outside the secured
walls of the military agencies. The laypersons can now employ security practices that
can protect against the most powerful adversaries.
There is no fundamental reason that a transaction must abort as
to abort transactions due to outside events, it is due to practical
consideration. After all, forcing all other nodes in a system to wait
for the node that experienced a nondeterministic event (such as a
hardware failure) to recover could bring a system to a painfully
Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài : Development and evaluation of a clinical algorithm to monitor patients on antiretrovirals in resource-limited settings using adherence, clinical and CD4 cell count criteria
Once an object has been instantiated, we can use the dot operator to invoke its methods
Note: A method may return a value or not
String s = new String(“Hello");
int count = s.length();
System.out.println("Length of s is " + count);
MORE ON VOLTAGE-PROCESSING TECHNIQUES
14.1 COMPARISON OF DIFFERENT VOLTAGE LEAST-SQUARES ALGORITHM TECHNIQUES Table 14.1-1 gives a comparison for the computer requirements for the different voltage techniques discussed in the previous chapter. The comparison includes the computer requirements needed when using the normal equations given by (4.1-30) with the optimum least-squares weight W given by (4.1-32). Table 14.
A routing algorithm constructs routing tables to forward communication packets based on network status information. Rapid inflation of the Internet increases demand for scalable and adaptive network routing algorithms. Conventional protocols such as the Routing Information Protocol (RIP) (Hedrick, 1988) and the Open Shortest-Path First protocol (OSPF) (Comer, 1995) are not adaptive algorithms; they because they only rely on hop count metrics to calculate shortest paths. In large networks, it is difficult to realize an adaptive algorithm based on conventional approaches. ...
For a given category, choose a small set of exemplars (or 'seed words') 2. Count co-occurrence of words and seed words within a corpus 3. Use a figure of merit based upon these counts to select new seed words 4. Return to step 2 and iterate n times 5. Use a figure of merit to rank words for category membership and o u t p u t a ranked list Our algorithm uses roughly this same generic structure, but achieves notably superior results, by changing the specifics of: what counts as co-occurrence; which figures of merit to use for...
RIP version 2 is not a new protocol—it is RIP Version 1 with some additional fields in the route update packet, key among them being subnet mask information in each route entry. The underlying DV algorithms in RIP-2 are identical to those in RIP-1, implying that RIP-2 still suffers from convergence problems and the maximum hop- count limit of 16 hops.
Traditional wisdom holds that once documents are turned into bag-of-words (unigram count) vectors, word orders are completely lost. We introduce an approach that, perhaps surprisingly, is able to learn a bigram language model from a set of bag-of-words documents. At its heart, our approach is an EM algorithm that seeks a model which maximizes the regularized marginal likelihood of the bagof-words documents.
Frequency counts from very large corpora, such as the Web 1T dataset, have recently become available for language modeling. Omission of low frequency n-gram counts is a practical necessity for datasets of this size. Naive implementations of standard smoothing methods do not realize the full potential of such large datasets with missing counts.
As the first step in an automated text summarization algorithm, this work presents a new method for automatically identifying the central ideas in a text based on a knowledge-based concept counting paradigm. To represent and generalize concepts, we use the hierarchical concept taxonomy WordNet. By setting appropriate cutoff values for such parameters as concept generality and child-to-parent frequency ratio, we control the amount and level of generality of concepts extracted from the text.
An important goal of computational linguistics has been to use linguistic theory to guide the construction of computationally efficient real-world natural language processing systems. At first glance, generalized phrase structure grammar (GPSG) appears to be a blessing on two counts. First, the precise formalisms of GPSG might be a direct and fransparent guide for parser design and implementation. Second, since GPSG has weak context-free generative power and context-free languages can be parsed in O(n ~) by a wide range of algorithms, GPSG parsers would appear to run in polynomial time.
We explore learning prepositionalphrase attachment in Dutch, to use it as a filter in prosodic phrasing. From a syntactic treebank of spoken Dutch we extract instances of the attachment of prepositional phrases to either a governing verb or noun. Using cross-validated parameter and feature selection, we train two learning algorithms, TB I and RIPPER, 011 making this distinction, based on unigram and bigram lexical features and a cooccurrence feature derived from WWW counts.