This guide explains the different methods of getting paid and the different levels of risks involved.
You should note that none of the methods outlined below will completely eliminate the payment
risks associated with international trade, so you should consider your preferred payment option
with care and hedge the risks along with appropriate credit insurance and credit checks on your
While almost any type of security can be used in a repo, funds prefer
to have U.S. Treasury or other government obligations as the collateral for
most of their transactions. For added security, the collateral must equal at
least 102 percent of the loan amount.
The transaction is called a repurchase agreement because the securities
are actually sold to the lender or investor at the beginning of the period of the
loan; the borrower agrees to repurchase the securities at the end of the loan
term, usually at the same price.
This paper proposes a novel method for learning probability models of subcategorization preference of verbs. We consider the issues of case dependencies and noun class generalization in a uniform way by employing the maximum entropy modeling method. We also propose a new model selection algorithm which starts from the most general model and gradually examines more specific models.
We present a system that automatically induces Selectional Preferences (SPs) for Latin verbs from two treebanks by using Latin WordNet. Our method overcomes some of the problems connected with data sparseness and the small size of the input corpora. We also suggest a way to evaluate the acquired SPs on unseen events extracted from other Latin corpora.
We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of ‘English as a Second or Other Language’ (ESOL) examination scripts. In particular, we use rank preference learning to explicitly model the grade relationships between scripts. A number of different features are extracted and ablation tests are used to investigate their contribution to overall performance. A comparison between regression and rank preference models further supports our method. ...
In this paper, we present a novel approach which incorporates the web-derived selectional preferences to improve statistical dependency parsing. Conventional selectional preference learning methods have usually focused on word-to-class relations, e.g., a verb selects as its subject a given nominal class.
We present the PONG method to compute selectional preferences using part-of-speech (POS) N-grams. From a corpus labeled with grammatical dependencies, PONG learns the distribution of word relations for each POS N-gram. From the much larger but unlabeled Google N-grams corpus, PONG learns the distribution of POS N-grams for a given pair of words. We derive the probability that one word has a given grammatical relation to the other. PONG estimates this probability by combining both distributions, whether or not either word occurs in the labeled corpus. ...
The computation of selectional preferences, the admissible argument values for a relation, is a well-known NLP task with broad applicability. We present L DA - SP, which utilizes LinkLDA (Erosheva et al., 2004) to model selectional preferences. By simultaneously inferring latent topics and topic distributions over relations, L DA - SP combines the beneﬁts of previous approaches: like traditional classbased approaches, it produces humaninterpretable classes describing each relation’s preferences, but it is competitive with non-class-based methods in predictive power. ...
This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word cooccurrences, are presented and evaluated on datasets of human plausibility judgements. Compared to previously proposed techniques, these models perform very competitively, especially for infrequent predicate-argument combinations where they exceed the quality of Web-scale predictions while using relatively little data. ...
This paper explores methods to alleviate the effect of lexical sparseness in the classiﬁcation of verbal arguments. We show how automatically generated selectional preferences are able to generalize and perform better than lexical features in a large dataset for semantic role classiﬁcation. The best results are obtained with a novel second-order distributional similarity measure, and the positive effect is specially relevant for out-of-domain data. Our ﬁndings suggest that selectional preferences have potential for improving a full system for Semantic Role Labeling. ...
In this paper we investigate a novel method to detect asymmetric entailment relations between verbs. Our starting point is the idea that some point-wise verb selectional preferences carry relevant semantic information. Experiments using WordNet as a gold standard show promising results. Where applicable, our method, used in combination with other approaches, signiﬁcantly increases the performance of entailment detection. A combined approach including our model improves the AROC of 5% absolute points with respect to standard models. ...
Various kinds of scored dependency graphs are proposed as packed shared data structures in combination with optimum dependency tree search algorithms. This paper classiﬁes the scored dependency graphs and discusses the speciﬁc features of the “Dependency Forest” (DF) which is the packed shared data structure adopted in the “Preference Dependency Grammar” (PDG), and proposes the “Graph Branch Algorithm” for computing the optimum dependency tree from a DF. This paper also reports the experiment showing the computational amount and behavior of the graph branch algorithm. ...
Previous work on the induction of selectional preferences has been mainly carried out for English and has concentrated almost exclusively on verbs and their direct objects. In this paper, we focus on class-based models of selectional preferences for German verbs and take into account not only direct objects, but also subjects and prepositional complements. We evaluate model performance against human judgments and show that there is no single method that overall performs best.
An important part of question answering is ensuring a candidate answer is plausible as a response. We present a ﬂexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the ﬂexibility of adding new and diverse features. Experimental results on a set of focused What ...? and Which ...? questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost 0.
The paper claims that the right attachment rules for phrases originally suggested by Frazier and Fodor are wrong, and that none of the subsequent patchings of the rules by syntactic methods have improved the situation. For each rule there are perfectly straightforward and indefinitely large classes of simple counter-examples. W e then examine suggestions by Ford et M., Schubert and Hirst which are quasi-semantic in nature and which we consider ingenious but unsatisfactory.
Cơ sở lý thuyết (THEORY)
• Mục đích - trình bày một biến lý thuyết chịu ảnh hưởng bởi
các nhân tố nào đó.
• Chúng ta quan sát chúng trong thực tiễn bằng các biến đại
• Kiểm định mối quan hệ giữa các biến bằng phương pháp
• Ví dụ:
- Lý thuyết: cầu phụ thuộc vào thị hiếu (taste/preference)
- Quan sát: doanh số phụ thuộc vào các proxiers?
This document is a sample configuration for setting up two Cisco routers back-to-back using Frame
Relay (FR) encapsulation. The routers are connected using data communications equipment (DCE) and a
data terminal equipment (DTE) serial cable. Back-to-back setups are useful in test environments. The
simplest and preferred method for configuring back-to-back setups is described in this document.
This is probably the slowest and least reliable method to getting consistent results on
Google as there may be thousands of sites ahead of your website. Your site is
competing with millions of existing sites, and even after submitting your site there is no
guarantee Google will list you since this is their least preferred method of finding new
sites. While you should not skip this first step, Google would rather discover you on their
own. How can you help Google do this? Link to other indexed sites; create good site
content; and use popular search terms. ...
After researching literature on addiction and film,
I chose the films for the study and viewed each one many
times, specifically looking for socioeconomic representations
of characters, treatment of different races, sexes, and sexual
preferences, methods of production as they relate to addicted
characters and drug usage, and the depiction of treatment/
self-help groups. I then outlined the narrative of each film
and compared the uses and meanings brought to addicts,
addiction, and substances. I found that these movies construct
a fairly unified image of treatment.
The Goodwin-Niering Center for Conservation Biology and Environmental Studies
at Connecticut College is a comprehensive, interdisciplinary program that builds on
one of the nation’s leading undergraduate environmental studies programs. The Center
fosters research, education, and curriculum development aimed at understanding
contemporary ecological challenges.