Best Android Apps: The Guide for Discriminating Downloaders
by Mike Hendrickson and Brian Sawyer. Sách lập trình tiếng anh mời các bạn tham khảo. First and foremost, I need to acknowledge my wife for more reasons than I can count. Without
her assistance, I never would have written this book, or if I had, I’d likely have given up upon
first receiving constructive criticism.
I’d like to thank my acquisitions editor, Laura Norman, and development editor,Todd Brakke,
whose constructive criticism I’ve been largely protected from by my wife.
In many computational linguistic scenarios, training labels are subjectives making it necessary to acquire the opinions of multiple annotators/experts, which is referred to as ”wisdom of crowds”. In this paper, we propose a new approach for modeling wisdom of crowds based on the Latent Mixture of Discriminative Experts (LMDE) model that can automatically learn the prototypical patterns and hidden dynamic among different experts. Experiments show improvement over state-of-the-art approaches on the task of listener backchannel prediction in dyadic conversations. ...
Large-scale discriminative machine translation promises to further the state-of-the-art, but has failed to deliver convincing gains over current heuristic frequency count systems. We argue that a principle reason for this failure is not dealing with multiple, equivalent translations. We present a translation model which models derivations as a latent variable, in both training and decoding, and is fully discriminative and globally optimised. Results show that accounting for multiple derivations does indeed improve performance.
We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are widely used by human translators in industrial settings.
Language models based on word surface forms only are unable to beneﬁt from available linguistic knowledge, and tend to suffer from poor estimates for rare features. We propose an approach to overcome these two limitations. We use factored features that can ﬂexibly capture linguistic regularities, and we adopt conﬁdence-weighted learning, a form of discriminative online learning that can better take advantage of a heavy tail of rare features.
We combine multiple word representations based on semantic clusters extracted from the (Brown et al., 1992) algorithm and syntactic clusters obtained from the Berkeley parser (Petrov et al., 2006) in order to improve discriminative dependency parsing in the MSTParser framework (McDonald et al., 2005).
While OOV is always a problem for most languages in ASR, in the Chinese case the problem can be avoided by utilizing character n-grams and moderate performances can be obtained. However, character ngram has its own limitation and proper addition of new words can increase the ASR performance. Here we propose a discriminative lexicon adaptation approach for improved character accuracy, which not only adds new words but also deletes some words from the current lexicon.
We present a simple and scalable algorithm for clustering tens of millions of phrases and use the resulting clusters as features in discriminative classifiers. To demonstrate the power and generality of this approach, we apply the method in two very different applications: named entity recognition and query classification. Our results show that phrase clusters offer significant improvements over word clusters. Our NER system achieves the best current result on the widely used CoNLL benchmark.
Conventional n-best reranking techniques often suffer from the limited scope of the nbest list, which rules out many potentially good alternatives. We instead propose forest reranking, a method that reranks a packed forest of exponentially many parses. Since exact inference is intractable with non-local features, we present an approximate algorithm inspired by forest rescoring that makes discriminative training practical over the whole Treebank.
We present a perceptron-style discriminative approach to machine translation in which large feature sets can be exploited. Unlike discriminative reranking approaches, our system can take advantage of learned features in all stages of decoding. We ﬁrst discuss several challenges to error-driven discriminative approaches. In particular, we explore different ways of updating parameters given a training example. We ﬁnd that making frequent but smaller updates is preferable to making fewer but larger updates. ...
We present a global discriminative statistical word order model for machine translation. Our model combines syntactic movement and surface movement information, and is discriminatively trained to choose among possible word orders. We show that combining discriminative training with features to detect these two different kinds of movement phenomena leads to substantial improvements in word ordering performance over strong baselines. Integrating this word order model in a baseline MT system results in a 2.4 points improvement in BLEU for English to Japanese translation. ...
A character-based measure of similarity is an important component of many natural language processing systems, including approaches to transliteration, coreference, word alignment, spelling correction, and the identiﬁcation of cognates in related vocabularies. We propose an alignment-based discriminative framework for string similarity. We gather features from substring pairs consistent with a character-based alignment of the two strings.
Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-ﬁne generative parser (Charniak, 2000). This method generates 50-best lists that are of substantially higher quality than previously obtainable. We used these parses as the input to a MaxEnt reranker (Johnson et al., 1999; Riezler et al.
We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second “reranking” model is then used to choose an utterance from these 1000-best lists. The reranking model makes use of syntactic features together with a parameter estimation method that is based on the perceptron algorithm. We describe experiments on the Switchboard speech recognition task. ...
This paper describes adaptations of unsupervised word sense discrimination techniques to the problem of name discrimination. These methods cluster the contexts containing an ambiguous name, such that each cluster refers to a unique underlying person or place. We also present new techniques to assign meaningful labels to the discovered clusters.
This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random ﬁelds (CRFs). The models are encoded as deterministic weighted ﬁnite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the beneﬁt of automatically selecting a relatively small feature set in just a couple of passes over the training data. ...
Discriminative methods have shown signiﬁcant improvements over traditional generative methods in many machine learning applications, but there has been diﬃculty in extending them to natural language parsing. One problem is that much of the work on discriminative methods conﬂates changes to the learning method with changes to the parameterization of the problem. We show how a parser can be trained with a discriminative learning method while still parameterizing the problem according to a generative probability model.
The paper describes a novel computational tool for multiple concept learning. Unlike previous approaches, whose major goal is prediction on unseen instances rather than the legibility of the output, our MPD (Maximally Parsimonious Discrimination) program emphasizes the conciseness and intelligibility of the resultant class descriptions, using three intuitive simplicity criteria to this end. We illustrate MPD with applications in componential analysis (in lexicology and phonology), language typology, and speech pathology. ...
We present a stochastic parsing system consisting of a Lexical-Functional Grammar (LFG), a constraint-based parser and a stochastic disambiguation model. We report on the results of applying this system to parsing the UPenn Wall Street Journal (WSJ) treebank. The model combines full and partial parsing techniques to reach full grammar coverage on unseen data. The treebank annotations are used to provide partially labeled data for discriminative statistical estimation using exponential models.
Concept taxonomies offer a powerful means for organizing knowledge, but this organization must allow for many overlapping and fine-grained perspectives if a general-purpose taxonomy is to reflect concepts as they are actually employed and reasoned about in everyday usage. We present here a means of bootstrapping finely-discriminating taxonomies from a variety of different starting points, or seeds, that are acquired from three different sources: WordNet, ConceptNet and the web at large.