1. Articles in category: Parsing

    49-72 of 562 « 1 2 3 4 5 6 ... 22 23 24 »
    1. Transition-Based Dependency Parsing With Pluggable Classifiers. (arXiv:1211.0074v1 [cs.CL])

      In principle, the design of transition-based dependency parsers makes it possible to experiment with any general-purpose classifier without other changes to the parsing algorithm. In practice, however, it often takes substantial software engineering to bridge between the different representations used by two software packages. Here we present extensions to MaltParser that allow the drop-in use of any classifier conforming to the interface of the Weka machine learning package, a wrapper for the TiMBL memory-based learner to this interface, and experiments on multilingual dependency parsing with a variety of classifiers. While earlier work had suggested that memory-based learners might be a ...
      Read Full Article
      Mentions: Weka
    2. Semantic object synchronous understanding for highly interactive interface

      A method and system provide a speech input mode which dynamically reports partial semantic parses, while audio captioning is still in progress. The semantic parses can be evaluated with an outcome immediately reported back to the user. The net effect is that task conventionally performed in the system turn are now carried out in the midst of the user turn thereby presenting a significant departure from the turn-taking nature of a spoken dialogue.
      Read Full Article
    3. A Novel System for Unlabeled Discourse Parsing in the RST Framework

      This paper presents UDRST, an unlabeled discourse parsing system in the RST framework. UDRST consists of a segmentation model and a parsing model. The segmentation model exploits subtree features to rerank N-best outputs of a base segmenter, which uses syntactic and lexical features in a CRF framework. In the parsing model, we present two algorithms for building a discourse tree from a segmented text: an incremental algorithm and a dual decomposition algorithm. Our system achieves 77.3% in the unlabeled score on the standard test set of the RST Discourse Treebank corpus, which improves 5.0% compared to HILDA [6 ...
      Read Full Article
    4. Semantic Role Labelling without Deep Syntactic Parsing

      This article proposes a method of Semantic Role Labelling for languages with no reliable deep syntactic parser and with limited corpora annotated with semantic roles. Reasonable results may be achieved with the help of shallow parsing, provided that features used for training such shallow parsers include both lexical semantic information (here: hypernymy) and syntactic information. Content Type Book ChapterPages 192-197DOI 10.1007/978-3-642-33983-7_19Authors Konrad Gołuchowski, University of Warsaw, PolandAdam Przepiórkowski, Institute of Computer Science, Polish Academy of Sciences, Poland Book Series Lecture Notes in Computer ScienceOnline ISSN 1611-3349Print ISSN 0302-9743 Book Series Volume Volume 7614/2012 Book Advances in Natural ...
      Read Full Article
    5. Shallow Parsing of Chinese Based on HMM Model

      Complete parsing is difficult to meet the need of precision and recall rate in Chinese. To address this problem, a new model for shallow parsing of Chinese is presented in this paper. We adopt Church theory and carry on Chinese phrases recognition based on HMM; improve the precision rate of sentences separation by improving the observance probabilities of HMM model and making use of the context information of the Chinese sentences. At the same time, by studying the rules of Chinese sentence, we extract some rules useful for ambiguity elimination. The experimental result indicates that the model based on HMM ...
      Read Full Article
    6. Dependency Parsing with Efficient Feature Extraction

      The fastest parsers currently can parse an average sentence in up to 2.5ms, a considerable improvement, since most of the older accuracy-oriented parsers parse only few sentences per second. It is generally accepted that the complexity of a parsing algorithm is decisive for the performance of a parser. However, we show that the most time consuming part of processing is feature extraction and therefore an algorithm which allows efficient feature extraction can outperform a less complex algorithm which does not. Our system based on quadratic Covington’s parsing strategy with efficient feature extraction is able to parse an average ...
      Read Full Article
    7. Evaluation of Computational Grammar Formalisms for Indian Languages. (arXiv:1209.1301v1 [cs.CL])

      Natural Language Parsing has been the most prominent research area since the genesis of Natural Language Processing. Probabilistic Parsers are being developed to make the process of parser development much easier, accurate and fast. In Indian context, identification of which Computational Grammar Formalism is to be used is still a question which needs to be answered. In this paper we focus on this problem and try to analyze different formalisms for Indian languages.
      Read Full Article
      Mentions: Indian
    8. Messenger based system and method to access a service from a backend system

      What is described is a system and method for accessing a backend service. The method includes receiving a message at a client; parsing the message into parts of the message using a natural language processor; interpreting the parts of the message; identifying a service and a backend system based on the interpreted parts of the message; and invoking the service from the backend system.
      Read Full Article
    9. Extraction of Semantic Relation Based on Feature Vector from Wikipedia

      In this paper, we propose a feature vector to extract semantic relations using dependency tree and parse tree. We exploit relation descriptions from infoboxes on Wikipedia documents. The features include part-of-speech, phrase label in dependency tree, and grammatical structure of phrase label, path of phrase label inherent in parse tree. In our experi ments, support vector machine and k-nearest neighbor are applied to extract relations from Wikipedia documents. Content Type Book ChapterPages 814-819DOI 10.1007/978-3-642-32695-0_78Authors Duc-Thuan Vo, Natural Language Processing Lab, School of Computer Engineering and Information Technology, University of Ulsan, KoreaCheol-Young Ock, Natural Language Processing Lab, School of ...
      Read Full Article
    10. Weighted Semantic Parsing: A Robust Approach to Interpretation of Natural Language Queries

      This paper focuses on a grammar-based approach to semantic interpretation, which combines the notions of robust and weighted parsing. In restricted domains of application in information extraction and natural language speech based information systems this approach shows acceptable performance. We present an overview of our research carried out within the recent project ISIS (Interaction through Speech with Information Systems) where techniques based on the above notions have been applied. Content Type Book ChapterPages 243-254DOI 10.1007/978-3-7908-1834-5_23Authors Afzal Ballim, LITH-MEDIA group, École Polytechnique Fédérale de Lausanne, IN-F Ecublens, 1015 Lausanne, SwitzerlandVincenzo Pallotta, LITH-MEDIA group, École Polytechnique Fédérale de Lausanne, IN-F ...
      Read Full Article
    11. Building Swahili Resource Grammars for the Grammatical Framework

      Grammatical Framework (GF) is a multilingual parsing and generation framework. In this paper, we describe the development of the Swahili Resource Grammar, a first in extending GF’s coverage with a Bantu language. The paper details the linguistic detail and considerations that have to be addressed whilst defining the grammars. The paper also describes an end-user application that uses the developed grammars to achieve multilinguality. Content Type Book ChapterPages 215-226DOI 10.1007/978-3-642-30773-7_13Authors Wanjiku Ng’ang’a, School of Computing and Informatics, University of Nairobi, Nairobi, Kenya Book Shall We Play the Festschrift Game?DOI 10.1007/978-3-642-30773-7Online ISBN 978-3-642-30773-7Print ...
      Read Full Article
      Mentions: Kenya Nairobi Bantu
    12. On Dependency Analysis via Contractions and Weighted FSTs

      Arc contractions in syntactic dependency graphs can be used to decide which graphs are trees. The paper observes that these contractions can be expressed with weighted finite-state transducers (weighted FST) that operate on string-encoded trees. The observation gives rise to a finite-state parsing algorithm that computes the parse forest and extracts the best parses from it. The algorithm is customizable to functional and bilexical dependency parsing, and it can be extended to non-projective parsing via a multi-planar encoding with prior results on high recall. Our experiments support an analysis of projective parsing according to which the worst-case time complexity of ...
      Read Full Article
    13. Brain-Inspired Knowledge-Driven Full Semantics Parsing

      Humans use semantics during parsing; so should computers. In contrast to phrase structure-based parsers, COGPARSE seeks to determine which meaning-bearing components are present in a text, using world knowledge and lexical semantics for construction grammar form selection, syntactic overlap processing, disambiguation, and confidence calculation. In a brain-inspired way, COGPARSE aligns parsing with the structure of the lexicon, providing a linguistic representation, parsing algorithm, associated linguistic theory, and preliminary metrics for evaluating parse quality. Given sufficient information on nuanced word and construction semantics, COGPARSE can also assemble detailed full-semantics meaning representations of input texts. Beyond the ability to determine which parses ...
      Read Full Article
    14. Apparatus and method of authoring animation through storyboard

      Described herein is an animation authoring apparatus and method thereof for authoring an animation. The apparatus includes a storyboard editor that provides a storyboard editing display that a user may interact with to edit a storyboard, and to store the edited storyboard. The apparatus further includes a parser to parse syntax of the edited storyboard, and a rendering engine to convert the edited storyboard into a graphic animation based on the parsed syntax of the edited storyboard.
      Read Full Article
    15. Syntax-based statistical translation model

      A statistical translation model (TM) may receive a parse tree in a source language as an input and separately output a string in a target language. The TM may perform channel operations on the parse tree using model parameters stored in probability tables. The channel operations may include reordering child nodes, inserting extra words at each node (e.g., NULL words) translating leaf words, and reading off leaf words to generate the string in the target language. The TM may assign a translation probability to the string in the target language.
      Read Full Article
    16. Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing. (arXiv:1206.6735v1 [cs.CL])

      We present a novel technique to remove spurious ambiguity from transition systems for dependency parsing. Our technique chooses a canonical sequence of transition operations (computation) for a given dependency tree. Our technique can be applied to a large class of bottom-up transition systems, including for instance Nivre (2004) and Attardi (2006).
      Read Full Article
    17. A Semi Supervised Learning Model for Mapping Sentences to Logical form with Ambiguous Supervision

      Semantic parsing is the task of mapping a natural sentence to a meaning representation. The limitation of semantic parsing is that it is very difficult to obtain annotated training data in which a sentence is paired with a semantic representation. To deal with this problem, we introduce a semi supervised learning model for semantic parsing with ambiguous supervision. The main idea of our method is to utilize a large amount of data, to enrich feature space with the maximum entropy model using our semantic learner. We evaluate the proposed models on standard corpora to show that our methods are suitable ...
      Read Full Article
    18. Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming

      Combinatory categorial grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses combinatory rules to combine these categories to parse a sentence. In this work we propose and implement a new approach to CCG parsing that relies on a prominent knowledge representation formalism, answer set programming (ASP) - a declarative programming paradigm. We formulate the task of CCG parsing as a planning problem and use an ASP computational tool to compute solutions that correspond to valid parses. Compared to other approaches, there is no need to implement a specific parsing algorithm ...
      Read Full Article
    19. Precision-biased Parsing and High-Quality Parse Selection. (arXiv:1205.4387v1 [cs.CL])

      We introduce precision-biased parsing: a parsing task which favors precision over recall by allowing the parser to abstain from decisions deemed uncertain. We focus on dependency-parsing and present an ensemble method which is capable of assigning parents to 84% of the text tokens while being over 96% accurate on these tokens. We use the precision-biased parsing task to solve the related high-quality parse-selection task: finding a subset of high-quality (accurate) trees in a large collection of parsed text. We present a method for choosing over a third of the input trees while keeping unlabeled dependency parsing accuracy of 97% on ...
      Read Full Article
    20. A Model-Driven Probabilistic Parser Generator. (arXiv:1205.3183v1 [cs.CL])

      Existing probabilistic scanners and parsers impose hard constraints on the way lexical and syntactic ambiguities can be resolved. Furthermore, traditional grammar-based parsing tools are limited in the mechanisms they allow for taking context into account. In this paper, we propose a model-driven tool that allows for statistical language models with arbitrary probability estimators. Our work on model-driven probabilistic parsing is built on top of ModelCC, a model-based parser generator, and enables the probabilistic interpretation and resolution of anaphoric, cataphoric, and recursive references in the disambiguation of abstract syntax graphs. In order to prove the expression power of ModelCC, we describe ...
      Read Full Article
    21. Parsing of Myanmar sentences with function tagging. (arXiv:1205.1603v1 [cs.CL])

      This paper describes the use of Naive Bayes to address the task of assigning function tags and context free grammar (CFG) to parse Myanmar sentences. Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system. Function tagging is a pre-processing step for parsing. In the task of function tagging, we use the functional annotated corpus and tag Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information. We propose Myanmar grammar rules and apply context free grammar (CFG) to find out the parse tree of ...
      Read Full Article
      Mentions: Myanmar Bayes
    49-72 of 562 « 1 2 3 4 5 6 ... 22 23 24 »
  1. Categories

    1. Default:

      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
  2. Popular Articles