Dictionary Information Research Articles

The Syntactic Constructions and the Semantic Classifications of Tibetan Verbs

This paper represents a departure from traditional Tibetan grammar in terms of the classification of verbs, for it constructs verb types and relevant syntactic rules based on syntax and semantics. The paper further distinguishes twelve types of Tibetan verbs according to the numbers of different arguments and the requirements of different syntactic properties. Therefore, the classification of the syntax and semantics of verbs Permits a detailed and overall reflection of the syntactic framework of all sorts of all kinds of Tibetan clause constructions, including word orders, case markers and syntactic particles. All the findings of syntactic and semantic classifications of Tibetan verbs can be applied directly to build a Tibetan grammatical information dictionary which is the infrastructure of natural Tibetan language processing.

A novel dictionary learning approach for multi-modality medical image fusion

Multi-modality medical image fusion technology can integrate the complementary information of different modality medical images, obtain more precise, reliable and better description of lesions. Dictionary learning based image fusion draws a great attention in researchers and scientists, for its high performance. The standard learning scheme uses entire image for dictionary learning. However, in medical images, the informative region takes a small proportion of the whole image. Most of the image patches have limited and redundant information. Taking all the image patches for dictionary learning brings lots of unvalued and redundant information, which can influence the medical image fusion quality. In this paper, a novel dictionary learning approach is proposed for image fusion. The proposed approach consists of three steps. Firstly, a novel image patches sampling scheme is proposed to obtain the informative patches. Secondly, a local density peaks based clustering algorithm is conducted to classify the image patches with similar image structure information into several patch groups. Each patch group is trained to a compact sub-dictionary by K-SVD. Finally the sub-dictionaries are combined to a complete, informative and compact dictionary. In this dictionary,only important and useful information which can effectively describe the medical image are selected. To show the efficiency of the proposed dictionary learning approach, the sparse coefficient vectors are estimated by a simultaneous orthogonal matching pursuit (SOMP) algorithm with the trained dictionary, and fused by max-L1 rules. The comparative experimental results and analyses reveal that the proposed method achieves better image fusion quality than existing state-of-the-art methods.

Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization

At a recent time, the web has become a valuable source of online consumer review however as the number of reviews is growing in high speed. It is infeasible for user to read all reviews to make a valuable or satisfying decision because the same features, people can write it contrary words or phrases. To produce a useful summary of domain synonyms words and phrase, need to be a group into same feature group. We focus on feature-based opinion mining problem and this paper mainly studies feature based product categorization from the number of users generated review available on the different website. First, a multi-feature segmentation method is proposed which segment multi-feature review sentences into the single feature unit. Second part of speech dictionary and context information is used to consider the irrelevant feature identification, sentiment words are used to identify the polarity of feature and finally an unsupervised clustering based product feature categorization method is proposed. Clustering is unsupervised machine learning approach that groups feature that have a high degree of similarity in a same cluster. The proposed approach provides satisfactory results and can achieve 100% average precision for clustering based product feature categorization task. This approach can be applicable to different product.

Impacts of the monolingual and bilingual dictionaries on the lexical errors committed by EFL learners in Hong Kong: a semantic analysis

Drawing upon semantic theories and concepts, including argument structure, componential analysis, descriptive meaning, and syntagmatic constraints, the present study analyses the four typical lexical errors committed by EFL learners in Hong Kong identified in a previous study conducted by Chan (TESOL Quarterly 44:295–319, 2010) and investigates whether the lexical information provided in monolingual and bilingual dictionaries alleviates or aggravates those four errors, respectively. The lexical information in monolingual dictionaries has been discovered to be efficacious in filling the gaps in learners’ lexical knowledge and assisting them in alleviating those errors, whilst that in bilingual dictionaries has been found to be capable of alleviating only the types of errors not influenced by the learners’ L1 but aggravating the errors influenced by the L1. The current study has provided pedagogical implications for the instruction of English vocabulary in the context of Hong Kong, albeit learners’ comprehension of the dictionary entries has to be investigated in the future to verify the findings of the study.

Joint Label Consistent Dictionary Learning and Adaptive Label Prediction for Semisupervised Machine Fault Classification

In this paper, we propose a semisupervised label consistent dictionary learning (SSDL) framework for machine fault classification. SSDL is a semisupervised extension of recent fully supervised label consistent dictionary learning approach, since the number of labeled machine data is usually limited in practice. To enable the supervised dictionary learning model to use both labeled and commonly readily available unlabeled data for enhancing performance, we propose to incorporate the merits of label prediction and present a joint label consistent dictionary learning and adaptive label prediction technique. In this setting, we first employ the existing label prediction model to estimate the labels of unlabeled training signals in a transductive fashion for enriching supervised prior. Then, we use predicted labeled data for label consistent dictionary learning. After that, we apply the discriminant sparse codes as the adaptive reconstruction weights for label prediction to update the estimated labels of unlabeled training data and the discriminative sparse codes matrix for label consistent dictionary learning so that classification performance can be enhanced. Thus, an informative dictionary, a sparse-code matrix, and an optimal multiclass classifier can be alternately obtained from one objective function. Besides, the tricky process of choosing optimal kernel width and neighborhood size can also be effectively voided in our scheme due to the adaptive weights. Extensive simulations on several machine fault datasets show that our SSDL method can deliver enhanced performance over other state-of-the-arts for machine fault classification.

Ментальные репрезентации психических состояний: феноменологические и экспериментальные характеристики

The article presents the results of a study of phenomenological and experimental characteristics of mental states representations (associative, conceptual, evaluative and imaginative indicators). The methods used to identify explicit and implicit indicators of associative connections in the context of «mental state– situation», for comparison of representations with the dictionary information, semantic differential, and others. The results indicate that mental states representations are formation, having a specific configuration and certain properties associated with the quality of mental state: the spatial organization, nuclear components, variative characteristics, stability of manifestations and independency from situations of human life.

A Hybrid Ranking Approach to Chinese Spelling Check

We propose a novel framework for Chinese Spelling Check (CSC), which is an automatic algorithm to detect and correct Chinese spelling errors. Our framework contains two key components: candidate generation and candidate ranking . Our framework differs from previous research, such as Statistical Machine Translation (SMT) based model or Language Model (LM) based model, in that we use both SMT and LM models as components of our framework for generating the correction candidates, in order to obtain maximum recall; to improve the precision, we further employ a Support Vector Machines (SVM) classifier to rank the candidates generated by the SMT and the LM. Experiments show that our framework outperforms other systems, which adopted the same or similar resources as ours in the SIGHAN 7 shared task; even comparing with the state-of-the-art systems, which used more resources, such as a considerable large dictionary, an idiom dictionary and other semantic information, our framework still obtains competitive results. Furthermore, to address the resource scarceness problem for training the SMT model, we generate around 2 million artificial training sentences using the Chinese character confusion sets, which include a set of Chinese characters with similar shapes and similar pronunciations, provided by the SIGHAN 7 shared task.

Corpora and Collocations in Chinese-English Dictionaries for Chinese Users

The paper identifies the major problems of the Chinese-English dictionary in representing collocational information after an extensive survey of nine dictionaries popular among Chinese users. It is found that the Chinese-English dictionary only provides the collocation types of v+n and v+n, but completely ignores those of v+adv, adj+n and adv+adj. And as a common practice, this kind of dictionary doesn’t give different collocates of synonymous equivalents. Besides, it provides collocational information on the basis of the headword, but not the equivalents. This leads to the suggestions for a new way of representing collocational information in Chinese-English dictionaries. The most important thing is that the Chinese-English dictionary should provide collocational information about the equivalents instead of the headword. All the five types of collocation are given the same status in a dictionary. Moreover, with corpus data, the selective restrictions of the English equivalents in meanings, frequency and semantic prosody should be explicitly represented to dictionary users by glosses, illustrative examples, collocation columns, etc. It is argued that by doing so, the dictionary will better meet the needs of the users.

Graph Based Constrained Semi-Supervised Learning Framework via Label Propagation over Adaptive Neighborhood

A new graph based constrained semi-supervised learning (G-CSSL) framework is proposed. Pairwise constraints (PC) are used to specify the types (intra- or inter-class) of points with labels. Since the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PC sets using the propagated soft labels from both labeled and unlabeled data by special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced performance. We also propose a Two-stage Sparse Coding, termed TSC, for achieving adaptive neighborhood for SLP. The first stage aims at correcting the possible corruptions in data and training an informative dictionary, and the second stage focuses on sparse coding. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained pairs using the sparse codes and outputted probabilistic values by SLP. Simulations on the synthetic and real datasets demonstrated the validity of our algorithms for data representation and image recognition, compared with other related state-of-the-art graph based semi-supervised techniques.

SemSin semantic and syntactic parser

The paper deals with the principle of operation for SemSin semantic and syntactic parser creating a dependency tree for the Russian language sentences. The parser consists of 4 blocks: a dictionary, morphological analyzer, production rules and lexical analyzer. An important logical part of the parser is pre-syntactical module, which harmonizes and complements morphological analysis results, separates the text paragraphs into individual sentences, and also carries out predisambiguation. Characteristic feature of the presented parser is an open type of control – it is done by means of a set of production rules. A varied set of commands provides the ability to both morphological and semantic-syntactic analysis of the sentence. The paper presents the sequence of rules usage and examples of their work. Specific feature of the rules is the decision making on establishment of syntactic links with simultaneous removal of the morphological and semantic ambiguity. The lexical analyzer provides the execution of commands and rules, and manages the parser in manual or automatic modes of the text analysis. In the first case, the analysis is performed interactively with the possibility of step-by-step execution of the rules and scanning the resulting parse tree. In the second case, analysis results are filed in an xml-file. Active usage of syntactic and semantic dictionary information gives the possibility to reduce significantly the ambiguity of parsing. In addition to marking the text, the parser is also usable as a tool for information extraction from natural language texts.

Skladenjski podatki v (slovenskem) narečnem slovarju

Značilnosti ustroja skladenjskih podatkovv splošnih eno-jezičnih slovarjih so izhodišče za obravnavo načina njihovega prikaza v slovenskih slovarjih knjižnega jezika in za oceno njihovega vpliva na narečno slovaropisje. Sondažna razčlemba eksplicitnih in implicitnih skladenjskih podatkov v slovenskih narečnih slovarjih pa je namenjena opredelitvi tistih prvin, ki so posebnost narečnega slova-ropisja (in v veliki meri tudi slovaropisja govorjenega jezika).

Characteristics of Chinese Grammar and the Grammatical Information in Chinese Dictionary for Second Language Learners

This paper discusses the Configuration and the Presentation of Grammatical Information in Chinese Dictionary for Second Language Learners on the basis of two Chinese dictionaries: one is the Commercial Press Learner’s Dictionary of Contemporary Chinese, the other one A Chinese Dictionary for Learners and Teachers. It emphasizes that the tagging of the sub category of Chinese part of speech should be considered in accordance with the characteristics of Chinese grammar. The features of rhythm restriction to Chinese syntax should also be included in grammatical information. It is important to make full use of the existing corpus for more suitable examples. It is also important to pay more attention to systematical consistency of grammatical tagging and the explanations of symmetrical words.

An Approach for Learning Expressive Ontologies in Medical Domain.

The access to medical information (journals, blogs, web-pages, dictionaries, and texts) has been increased due to availability of many digital media. In particular, finding an appropriate structure that represents the information contained in texts is not a trivial task. One of the structures for modeling the knowledge are ontologies. An ontology refers to a conceptualization of a specific domain of knowledge. Ontologies are especially useful because they support the exchange and sharing of information as well as reasoning tasks. The usage of ontologies in medicine is mainly focussed in the representation and organization of medical terminologies. Ontology learning techniques have emerged as a set of techniques to get ontologies from unstructured information. This paper describes a new ontology learning approach that consists of a method for the acquisition of concepts and its corresponding taxonomic relations, where also axioms disjointWith and equivalentClass are learned from text without human intervention. The source of knowledge involves files about medical domain. Our approach is divided into two stages, the first part corresponds to discover hierarchical relations and the second part to the axiom extraction. Our automatic ontology learning approach shows better results compared against previous work, giving rise to more expressive ontologies.

Bilinear low-rank coding framework and extension for robust image recovery and feature representation

We mainly study the low-rank image recovery problem by proposing a bilinear low-rank coding framework called Tensor Low-Rank Representation. For enhanced low-rank recovery and error correction, our method constructs a low-rank tensor subspace to reconstruct given images along row and column directions simultaneously by computing two low-rank matrices alternately from a nuclear norm minimization problem, so both column and row information of data can be effectively preserved. Our bilinear approach seamlessly integrates the low-rank coding and dictionary learning into a unified framework. Thus, our formulation can be treated as enhanced Inductive Robust Principal Component Analysis with noise removed by low-rank representation, and can also be considered as the enhanced low-rank representation with a clean informative dictionary via low-rank embedding. To enable our method to include outside images, the out-of-sample extension is also presented by regularizing the model to correlate image features with the low-rank recovery of the images. Comparison with other criteria shows that our model exhibits stronger robustness and enhanced performance. We also use the outputted bilinear low-rank codes for feature learning. Two unsupervised local and global low-rank subspace learning methods are proposed for extracting image features for classification. Simulations verified the validity of our techniques for image recovery, representation and classification.

Exploring teachers' oral explanations of new English lexical items in a Chinese university: Comparisons with dictionary information

Abstract We investigated three university teachers' oral explanations of unfamiliar lexical items in a Chinese university English as a foreign language (L2) context. Previous research on explanation of lexis has focused on the amount or functions of teachers' first language (L1) use, or in the field of glossing in written texts. In this exploratory research we compared teacher oral explanations to the information provided by dictionaries, examined whether the teachers provided information in L1 or L2 or both, and explored the type of information they provided. We did this comparison not to evaluate the teachers' competence but to gain insights into the challenges teachers face when putting across the meaning of words which may or may not have direct equivalence in both languages. Greater awareness of these challenges might inform teacher education programmes. Our findings suggest both close comparisons between teacher and dictionary information and interesting divergences. Further research resulting from this exploratory study is suggested.

Joint patch clustering-based dictionary learning for multimodal image fusion

Constructing a good dictionary is the key to a successful image fusion technique in sparsity-based models. An efficient dictionary learning method based on a joint patch clustering is proposed for multimodal image fusion. To construct an over-complete dictionary to ensure sufficient number of useful atoms for representing a fused image, which conveys image information from different sensor modalities, all patches from different source images are clustered together with their structural similarities. For constructing a compact but informative dictionary, only a few principal components that effectively describe each of joint patch clusters are selected and combined to form the over-complete dictionary. Finally, sparse coefficients are estimated by a simultaneous orthogonal matching pursuit algorithm to represent multimodal images with the common dictionary learned by the proposed method. The experimental results with various pairs of source images validate effectiveness of the proposed method for image fusion task.

Project-Based As-Needed Information Retrieval from Unstructured AEC Documents

AbstractWith the increasing complexity of architecture, engineering, and construction (AEC) projects and fast track execution of project works, written documents are becoming more and more important for project coordination, communication, and works control. Finding all the relevant information from unstructured construction documents is critical to various management tasks such as work planning, progress control, and claims. A framework is proposed in this research to retrieve project-wide as-needed information from AEC documents. Through this framework, improvement in the levels of precision and recall in the information retrieval process can be made effective through the use of a project-specific term dictionary and dependency grammar parsing information of textual documents. Their effectiveness is demonstrated through a series of experimental tests conducted on a real life building redevelopment project with different information retrieval and ranking strategies. The results and findings are presented...

Korean and English ‘dictionary’ questions: what does the public want to know?

This paper proposes an alternative to questionnaire surveys and other methods of investigating dictionary user expectations, wants and needs: the content analysis of dictionary-related queries on online general-purpose Q&A sites. Web postings are anonymous and are not associated with any particular institution or dictionary developer, so they tend to reflect the ordinary concerns of users looking for unbiased practical answers in authentic contexts of dictionary use. Analysis of a random sample of approximately 500 English-medium Yahoo! Answers queries and 500 Korean-medium Naver Knowledge-iN queries found a greater tendency to discuss dictionary choice on the Korean site, and more postings about word meanings and origins, and dictionary use for word games, on the UK/Irish site. Many Korean queries concerned dictionaries for English language learning and comparisons between different types of dictionary, whilst Yahoo! queries were more likely to reject dictionary information as incomplete or incorrect.

Design of Automatic Test Platform for the Gyroscope Group Based on Pertinence Matrix

Aiming the problem of the armored vehicle's gun control system that there are many kinds of internal devices, complex fault reasons ,but no all-around and online fault diagnosis and state inspection mean, The automatic test platform for the gyroscope group with performance test and fault diagnosis for component and circuit is designed .The platform based on dependency matrix and optimal criterion of the maximum failure feature information entropy optimize test points ,choose optimal test points design. Performance test module is created and provides test result information for fault dictionary in fault diagnosis module. Automatic test platform is able to locate the circuit component failure.The platform is tested by actual vehicle experiment, and the results prove the reliability and validity of the platform.

An Algorithm of Network Sensitive Information Features Extracting

In the vast ocean of data, how people can learn from the best, to eliminate the dross, the Internet age has become a major issue, also facing great challenge for data processing, and it is the key to develop the national network economy. For sensitive information filtering time lag, low accuracy, poor self-adaptability to the Internet, Chinese text media (webpage, micro-blogging, forum, etc.) for the study, using the technology of the opinion mining and natural language processing, study of sensitive information feature extraction algorithm to reveal the interrelationship of sensitive information and sensitive dictionary, providing technical support for sensitive dictionary and sensitive information recognition.