Grapheme-to-phoneme conversion (G2P) of names is an important and challenging problem. The correct pronunciation of a name is often reflected in its transliterations, which are expressed within a different phonological inventory. We investigate the problem of using transliterations to correct errors produced by state-of-the-art G2P systems. We present a novel re-ranking approach that incorporat...

We present ERSS 2005, our entry to this year’s DUC competition. With only slight modifications from last year’s version to accommodate the more complex context information present in DUC 2005, we achieved a similar performance to last year’s entry, ranking roughly in the upper third when examining the ROUGE-1 and Basic Element score. We also participated in the additional manual evaluation base...

Current re-ranking algorithms for machine translation rely on log-linear models, which have the potential problem of underfitting the training data. We present BoostedMERT, a novel boosting algorithm that uses Minimum Error Rate Training (MERT) as a weak learner and builds a re-ranker far more expressive than log-linear models. BoostedMERT is easy to implement, inherits the efficient optimizati...

It is a common practice among Web 2.0 services to allow users to rate items on their sites. In this paper, we first point out the flaws of the popular methods for user-rating based ranking of items, and then argue that two well-known Information Retrieval (IR) techniques, namely the Probability Ranking Principle and Statistical Language Modelling, provide simple but effective solutions to this ...

The implementation of profile matching method in selecting the best employees at PT Jendela Digital Indonesia aims to assist managers making decisions with right calculations and criteria. Employees are one factors that play an important role advancing company. Employee performance affects company obtaining profits. To spur employee performance, selects every period by giving appreciation bonus...

This paper presents a representation theory for permutation-valued functions, which in their general form can also be called listwise ranking functions. Pointwise ranking functions assign a score to each object independently, without taking into account the other objects under consideration; whereas listwise loss functions evaluate the set of scores assigned to all objects as a whole. In many s...

Ranking documents in response to users' information needs is a challenging task, due, in part, to the dynamic nature of users' interests with respect to a query. We hypothesize that the interests of a given user are similar to the interests of the broader community of which he or she is a part and propose an innovative method that uses social media to characterize the interests of the community...

In this paper, we propose a co-ranking algorithm that trains listwise ranking functions using unlabeled data simultaneously with a small number of labeled data. The coranking algorithm is based on the co-training paradigm that is a very common scheme in the semi-supervised classification framework. First, we use two listwise ranking methods to construct base ranker and assistant ranker, respect...

The blogosphere is a part of the World Wide Web, enhanced with several characteristics that differentiate blogs from traditional websites. The number of different authors, the multitude of user-provided tags, the inherent connectivity between blogs and bloggers, the high update rate, and the time information attached to each post are some of the features that can be exploited in various informa...

One-class collaborative filtering or collaborative ranking with implicit feedback has been steadily receiving more attention, mostly due to the “oneclass” characteristics of data in various services, e.g., “like” in Facebook and “bought” in Amazon. Previous works for solving this problem include pointwise regression methods based on absolute rating assumptions and pairwise ranking methods with ...