Selecting text

Xem 1-20 trên 206 kết quả Selecting text
  • 3D TEXT Chỉ làm việc với photoshop CS 1.) Gõ nội dung chữ cần gõ , như cách gõ trên thì hãy đọc lại bài cách gõ chữ theo định hướng của đường Path * Sau khi gõ xong chữ thì vào : Layer Type Convert to Shape 2.) Chọn công cụ Path Selection Tool , sau đó kích chuột và đẩy chữ vào trong đường Path

    pdf4p hamberger5k 08-08-2010 66 6   Download

  • This paper discusses a decision-tree approach to the problem of assigning probabilities to words following a given text. In contrast with previous decision-tree language model attempts, an algorithm for selecting nearly optimal questions is considered. The model is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the well-known trigram model.

    pdf4p bunrieu_1 18-04-2013 31 4   Download

  • This paper deals with an application of automatic titling. The aim of such application is to attribute a title for a given text. So, our application relies on three very different automatic titling methods. The first one extracts relevant noun phrases for their use as a heading, the second one automatically constructs headings by selecting words appearing in the text, and, finally, the third one uses nominalization in order to propose informative and catchy titles. Experiments based on 1048 titles have shown that our methods provide relevant titles. ...

    pdf4p bunthai_1 06-05-2013 22 3   Download

  • 1,3 Bind và xem cá nhân Text Boxes Dựa Off một lựa chọn Danh sách Hộp khoản Sử dụng một hộp danh sách tương tự với một trong các trang trước How-To, trong How-To, bạn sẽ học cách tạo OleDbDataAdapters bổ sung và DataSets và nối kết chúng để cá nhân hộp văn bản cho dữ liệu xem.

    pdf1p luvpro 06-08-2010 41 2   Download

  • We address the problem of selecting nondomain-specific language model training data to build auxiliary language models for use in tasks such as machine translation. Our approach is based on comparing the cross-entropy, according to domainspecific and non-domain-specifc language models, for each sentence of the text source used to produce the latter language model. We show that this produces better language models, trained on less data, than both random data selection and two other previously proposed methods. ...

    pdf5p hongdo_1 12-04-2013 20 2   Download

  • We evaluate the effect of adding parse features to a leading model of preposition usage. Results show a significant improvement in the preposition selection task on native speaker text and a modest increment in precision and recall in an ESL error detection task. Analysis of the parser output indicates that it is robust enough in the face of noisy non-native writing to extract useful information.

    pdf6p hongdo_1 12-04-2013 19 2   Download

  • Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. ...

    pdf11p hongdo_1 12-04-2013 21 2   Download

  • In text categorization, feature selection (FS) is a strategy that aims at making text classifiers more efficient and accurate. However, when dealing with a new task, it is still difficult to quickly select a suitable one from various FS methods provided by many previous studies. In this paper, we propose a theoretic framework of FS methods based on two basic measurements: frequency measurement and ratio measurement. Then six popular FS methods are in detail discussed under this framework.

    pdf9p hongphan_1 14-04-2013 13 2   Download

  • Since facts or statements in a hedge or negated context typically appear as false positives, the proper handling of these language phenomena is of great importance in biomedical text mining. In this paper we demonstrate the importance of hedge classification experimentally in two real life scenarios, namely the ICD9-CM coding of radiology reports and gene name Entity Extraction from scientific texts. We analysed the major differences of speculative language in these tasks and developed a maxent-based solution for both the free text and scientific text processing tasks. ...

    pdf9p hongphan_1 15-04-2013 18 2   Download

  • Semantic relations between text concepts denote the core elements of lexical semantics. This paper presents a model for the automatic detection of INTENTION semantic relation. Our approach first identifies the syntactic patterns that encode intentions, then we select syntactic and semantic features for a SVM learning classifier. In conclusion, we discuss the application of INTENTION relations to Q&A.

    pdf6p bunbo_1 17-04-2013 17 2   Download

  • We define a new feature selection score for text classification based on the KL-divergence between the distribution of words in training documents and their classes. The score favors words that have a similar distribution in documents of the same class but different distributions in documents of different classes. Experiments on two standard data sets indicate that the new method outperforms mutual information, especially for smaller categories.

    pdf4p bunbo_1 17-04-2013 26 2   Download

  • Selecting the most appropriate sense for an ambiguous word in a sentence is a central problem in Natural Language Processing. In this paper, we present a method that attempts to disambiguate all the nouns, verbs, adverbs and adjectives in a text, using the senses provided in WordNet.

    pdf7p bunrieu_1 18-04-2013 20 2   Download

  • We notion of argue that in domains can be where it a strong can be Mann and Moore [1981], on the other hand, while assembling texts dynamically to suit their audience, do so by "over-generating" the set of facts that will be related, and then passing them all through a special filter, leaving out those that are judged to be already known to the audience and letting through those that are new. McKeown [1981] uses a similar technique -- her generator, like Mann and Moore's, must examine every potentially mentionable object in the domain data base and make...

    pdf7p bungio_1 03-05-2013 14 2   Download

  • This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approach is to reduce the tasks of content selection (“what to say”) and surface realization (“how to say”) into a common parsing problem.

    pdf10p nghetay_1 07-04-2013 28 1   Download

  • This paper discusses sampling strategies for building a dependency-analyzed corpus and analyzes them with different kinds of corpora. We used the Kyoto Text Corpus, a dependency-analyzed corpus of newspaper articles, and prepared the IPAL corpus, a dependency-analyzed corpus of example sentences in dictionaries, as a new and different kind of corpus. The experimental results revealed that the length of the test set controlled the accuracy and that the longest-first strategy was good for an expanding corpus, but this was not the case when constructing a corpus from scratch. ...

    pdf8p hongvang_1 16-04-2013 20 1   Download

  • Kernel-based learning (e.g., Support Vector Machines) has been successfully applied to many hard problems in Natural Language Processing (NLP). In NLP, although feature combinations are crucial to improving performance, they are heuristically selected. Kernel methods change this situation. The merit of the kernel methods is that effective feature combination is implicitly expanded without loss of generality and increasing the computational costs.

    pdf8p bunbo_1 17-04-2013 17 1   Download

  • This position paper argues for an interactive approach to text understanding. The proposed model extends an existing semantics-based text authoring system by using the input text as a source of information to assist the user in re-authoring its content. The approach permits a reliable deep semantic analysis by combining automatic information extraction with a minimal amount of human intervention.

    pdf4p bunbo_1 17-04-2013 18 1   Download

  • In many types of technical texts, meaning is embedded in noun compounds. A language understanding program needs to be able to interpret these in order to ascertain sentence meaning. We explore the possibility of using an existing lexical hierarchy for the purpose of placing words from a noun compound into categories, and then using this category membership to determine the relation that holds between the nouns. In this paper we present the results of an analysis of this method on twoword noun compounds from the biomedical domain, obtaining classification accuracy of approximately 90%. ...

    pdf8p bunmoc_1 20-04-2013 18 1   Download

  • level knowledge sources can then be used to select a decision from the candidate set for each word image. In this paper, we propose that visual inter-word constraints can be used to facilitate candidate selection. Visual inter-word constraints provide a way to link word images inside the text page, and to interpret t h e m systematically. Introduction The objective of visual text recognition is to transform an arbitrary image of text into its symbolic equivalent correctly.

    pdf3p bunmoc_1 20-04-2013 15 1   Download

  • Our goal is to identify the features that predict cue selection and placement in order to devise strategies for automatic text generation. Much previous work in this area has relied on ad hoc methods. Our coding scheme for the exhaustive analysis of discourse allows a systematic evaluation and refinement of hypotheses concerning cues. We report two results based on this analysis: a comparison of the distribution of Sn~CE and BECAUSEin our corpus, and the impact of embeddedness on cue selection. ...

    pdf6p bunmoc_1 20-04-2013 19 1   Download


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