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. ...
This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model (Roark, 2001a), and experimental results show that it gives competitive performance to the generative model on parsing the Penn treebank. We demonstrate that training a perceptron model to combine with the generative model during search provides a 2.
This paper introduces new learning algorithms for natural language processing based on the perceptron algorithm. We show how the algorithms can be efﬁciently applied to exponential sized representations of parse trees, such as the “all subtrees” (DOP) representation described by (Bod 1998), or a representation tracking all sub-fragments of a tagged sentence. We give experimental results showing signiﬁcant improvements on two tasks: parsing Wall Street Journal text, and namedentity extraction from web data. ...
Một trong những câu hỏi chúng tôi nêu ra trong
chương 3 là: "Làm thế nào để chúng ta xác định ma
trận trọng số và hệ số hiệu chỉnh cho các mạng
perceptron với nhiều đầu vào, trường hợp không thể
hình dung ranh giới quyết định?" Trong chương này,
chúng tôi sẽ mô tả một thuật toán cho phép đào tạo
mạng perceptron, để giải quyết vấn đề phân loại.
We wish to construct a system which possesses so-called associative memory.
This is definable generally as a process by which an input, considered as a
“key”, to a memory system is able to evoke, in a highly selective fashion, a
specific response associated with that key, at the system output. The signalresponse
association should be “robust”, that is, a “noisy” or “incomplete”
input signal should none the less invoke the correct response—or at least
an acceptable response. Such a system is also called a content addressable
We propose a set of open-source software modules to perform structured Perceptron Training, Prediction and Evaluation within the Hadoop framework. Apache Hadoop is a freely available environment for running distributed applications on a computer cluster. The software is designed within the Map-Reduce paradigm. Thanks to distributed computing, the proposed software reduces substantially execution times while handling huge data-sets. The distributed Perceptron training algorithm preserves convergence properties, thus guaranties same accuracy performances as the serial Perceptron. ...
This paper presents an approach to automatically build a semantic perceptron net (SPN) for topic spotting. It uses context at the lower layer to select the exact meaning of key words, and employs a combination of context, co-occurrence statistics and thesaurus to group the distributed but semantically related words within a topic to form basic semantic nodes. The semantic nodes are then used to infer the topic within an input document. Experiments on Reuters 21578 data set demonstrate that SPN is able to capture the semantics of topics, and it performs well on topic spotting task. ...
Sentence ﬂuency is an important component of overall text readability but few studies in natural language processing have sought to understand the factors that deﬁne it. We report the results of an initial study into the predictive power of surface syntactic statistics for the task; we use ﬂuency assessments done for the purpose of evaluating machine translation. We ﬁnd that these features are weakly but signiﬁcantly correlated with ﬂuency. Machine and human translations can be distinguished with accuracy over 80%.
For Chinese POS tagging, word segmentation is a preliminary step. To avoid error propagation and improve segmentation by utilizing POS information, segmentation and tagging can be performed simultaneously. A challenge for this joint approach is the large combined search space, which makes efﬁcient decoding very hard. Recent research has explored the integration of segmentation and POS tagging, by decoding under restricted versions of the full combined search space.
Standard approaches to Chinese word segmentation treat the problem as a tagging task, assigning labels to the characters in the sequence indicating whether the character marks a word boundary. Discriminatively trained models based on local character features are used to make the tagging decisions, with Viterbi decoding ﬁnding the highest scoring segmentation. In this paper we propose an alternative, word-based segmentor, which uses features based on complete words and word sequences.
This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the application being the recovery of named-entity boundaries in a corpus of web data. The ﬁrst approach uses a boosting algorithm for ranking problems. The second approach uses the voted perceptron algorithm. Both algorithms give comparable, signiﬁcant improvements over the maximum-entropy baseline. The voted perceptron algorithm can be considerably more efﬁcient to train, at some cost in computation on test examples.
This paper describes POS tagging experiments with semi-supervised training as an extension to the (supervised) averaged perceptron algorithm, ﬁrst introduced for this task by (Collins, 2002). Experiments with an iterative training on standard-sized supervised (manually annotated) dataset (106 tokens) combined with a relatively modest (in the order of 108 tokens) unsupervised (plain) data in a bagging-like fashion showed signiﬁcant improvement of the POS classiﬁcation task on typologically different languages, yielding better than state-of-the-art results for English and Czech (4.
We propose a cascaded linear model for joint Chinese word segmentation and partof-speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efﬁciently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved accuracies on both segmentation only and joint segmentation and part-of-speech tagging. On the Penn Chinese Treebank 5.0, we obtain an error reduction of 18.
Hàm tương quan phi chu kỳ và độ phức tạp của các dãy phi tuyến dùng CDMA thế hệ mới. Chính Warren McCulloch cũng đã chuyển từ tâm lý học sang toán học, rồi từ toán học sang kỹ thuật.
Trong thời gian 1940 và 1950 có những thành tựu như kiến trúc máy tính của von Neumann, lý thuyết trò chơi và tế bào tự hành của Wolfram; Ashby và von Foerster có phân tích sự tự tổ chức; người máy tự trị của Braitenberg và mạng thần kinh nhân tạo, perceptrons, classifiers... của McCulloch ...
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. ...
Chapter 3: Artificial neural networks Introduction; ANN representations, Perceptron Training, Multilayer networks and Backpropagation algorithm, Remarks on the Backpropagation algorithm, Neural network application development, Benefits and limitations of ANN, ANN Applications.
Điều này không đúng cho đầu vào thứ 4, nhưng thuật toán hội tụ trong lần thứ 6. Giá trị cuối cùng là:
W(6) = [-2 -3] và b(6) = 1. Đển đây kết thúc sự tính toán bằng tay. Bây giờ ta cần làm thế nào để sử dụng hàm huấn luyện? Theo mã định nghĩa perceptron như đã chỉ ra trên hình vẽ trước, với giá trị ban đầu của hàm trọng và độ dốc bằng 0, ta có:
net = newp(l-2 2;-2 +2],1);
Quan sát giá trị của đầu vào đơn....
In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman ﬁlter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images. Many complexities arise in visual processing that are not present in onedimensional prediction problems: the scene may be cluttered with backKalman Filtering and Neural Network
In this chapter, we consider another application of the extended Kalman ﬁlter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.