On semantics-based spatial data preprocessing: a case study in non-ortho RS images mosaic

Semantic information and syntactic information are two essential dimensions of language. In recent years, most researches focus on extracting semantic information or how to extract useful information from syntactic dependency tree. As the semantic representations are closely related to syntactic ones, how to effectively integrate semantic and syntactic information is still a subject worth studying. We propose attention mechanism based on graph convolutional networks. The model integrates syntactic dependence information into semantic features and automatically achieve a balance between semantic information and syntactic information. We conducted experiments on the task of sentence-level relation extraction, and the results show that our model performs better than some previous methods.

The information really useful to humans must be the trinity of its three components, the form termed syntactic information, the meaning termed semantic information, and the utility termed pragmatic information. But the theory of information set up by Shannon in 1948 is a statistical theory of syntactic information. Thus, the trinity of information theories needs be established as urgently as possible. Such a theory of semantic information will be presented in the paper and it will also be proved that it is the semantic information that is the unique representative of the trinity. This is why the title of the paper is set to “a theory of semantic information” without mentioning the pragmatic information.

ALSC (Aspect-level Sentiment Classification) is a fine-grained task in the field of NLP (Natural Language Processing) which aims to identify the sentiment toward a given aspect. In addition to exploiting the sentence semantics and syntax, current ALSC methods focus on introducing external knowledge as a supplementary to the sentence information. However, the integration of the three categories of information is still challenging. In this paper, a novel method is devised to effectively combine sufficient semantic and syntactic information as well as use of external knowledge. The proposed model contains a sentence encoder, a semantic learning module, a syntax learning module, a knowledge enhancement module, an information fusion module and a sentiment classifier. The semantic information and syntactic information are respectively extracted via a self-attention network and a graphical convolutional network. Specifically, the KGE (Knowledge Graph Embedding) is employed to enhance the feature representation of the aspect. Then, the attention-based gate mechanism is taken to fuse three types of information. We evaluated the proposed model on three benchmark datasets and the experimental results establish strong evidence of high accuracy.

Sentence classification is a significant task in natural language processing (NLP) and is applied in many fields. The syntactic and semantic properties of words and phrases often determine the success of sentence classification. Previous approaches based on sequential modeling mainly ignored the explicit syntactic structures in a sentence. In this paper, we propose a Syntax-Aware Transformer (SA-Trans), which integrates syntactic information in the transformer and obtains sentence embeddings by combining syntactic and semantic information. We evaluate our SA-Trans on four benchmark classification datasets (i.e., AG’News, DBpedia, ARP, ARF), and the experimental results manifest that our SA-Trans model achieves competitive performance compared to the baseline models. Finally, the case study further demonstrates the importance of syntactic information for the classification task.KeywordsSyntaxTransformerSentence classification

Creation of a consistent 3D model of a city requires accurate data. Usually, accuracy assurance problems of data are solved by time consuming and expensive process of collecting and aligning with ground control points (GCP). Therefore, alternative methods become important. Using existing Geographic Information Systems (GIS) databases may decrease the time and cost of creating a reference dataset by reducing the number of GCPs required for producing high quality 3D data or GIS databases can serve as reference data. For this purpose, new spatial data analysis methods are needed to assure that GIS databases are of high-quality. In this paper, we propose a novel methodology and its sample development for comparative spatial analysis of digitized point cloud and the corresponding GIS database in order to statistically assess opportunities to align Mobile Mapping Systems (MMS) data with existing GIS databases or to improve involved datasets. The method is evaluated using LiDAR data provided by Estonian company Reach-U Ltd. and GIS database layers from different Estonian open and closed databases.

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

Prior literature has debated whether syntax is separable from semantics in the brain. Using functional magnetic resonance imaging and multi-voxel pattern analysis, our previous studies investigated brain activity during morpho-syntactic versus semantic processing. These studies only detected semantic specialization in activation patterns and no syntactic specialization in 5- to 6-year-old and 7- to 8-year-old children. To examine if older children who have mastered morpho-syntactic skills would show specialization for syntax, the current study examined 64 9- to 10-year-old children using the same design and analyses. We observed that only the left IFG pars opercularis was sensitive to syntactic but not semantic information, supporting the hypothesis that this region serves as a core region for syntax. In addition, the left STG which has been implicated in the integration of semantics and syntax, as well as the left MTG and IFG pars triangularis which have been implicated in semantics, were sensitive to both semantic and syntactic information with no evidence of specialization. These findings suggest a lexicalized view of syntax, which argues that semantically sensitive regions are also critical regions for syntactic processing during language comprehension.

A large part of the development effort of compute-intensive applications is devoted to optimization, i.e., achieving the computation within a finite budget of time, space or energy. Given the complexity of modern architectures, writing simulation applications is often a two-step workflow. Firstly, developers design a sequential program for algorithmic tuning and debugging purposes. Secondly, experts optimize and exploit possible approximations of the original program to scale to the actual problem size. This second step is a tedious, time-consuming and error-prone task. In this paper we investigate language extensions and compiler tools to achieve that task semi-automatically in the context of approximate computing. We identified the semantic and syntactic information necessary for a compiler to automatically handle approximation and adaptive techniques for a particular class of programs. We propose a set of language extensions generic enough to provide the compiler with the useful semantic information when approximation is beneficial. We implemented the compiler infrastructure to exploit these extensions and to automatically generate the adaptively approximated version of a program. We provide an experimental study of the impact and expressiveness of our language extension set on various applications.

Sentence similarity measures have taken an increasingly important role in a variety of applications of text knowledge presentation and discovery. Current methods utilize only semantic or syntactic information. We propose a novel method to calculate sentence similarity, which takes into account both semantic information and word order. The experimental results show that our method outperforms existing methods.

The Rational Polynomial Coefficients (RPC) provided with the IKONOS images contains a large error and they need Ground Control Point (GCP) refinement. To present the technique of refinement of RPCs by the application of some appropriate transformation algorithm with some suitable number of GCPs in proper constellation in an optimal way to achieve high geometric accuracy during spatial data acquisition from IKONOS stereo image is the objective of this paper. From this study it was found that GCP refinement of RPCs by affine transformation with four GCPs in proper constellation is optimal for the orientation of the image pair under study, it was also found that at least two redundant GCPs are necessary for proper refinement by a particular transformation algorithm.

Motives: The need for sustainable urban development, including an improvement in residents’ quality of life, requires ongoing urban diagnostics. Assessments of multi-family housing estates play a very important role in this process. Population growth influences the expansion of housing estates in limited urban space. The extent to which spatial and functional structures (SFS) in housing developments meet the residents’ current needs should be evaluated. These needs undergo dynamic change and are influenced by economic, socio-cultural, sanitary, and ecological factors. Aim: The main objective of this study was to develop a methodology for assessing SFS solutions in open spaces in multi-family residential estates (MFREs) based on a complete list of SFS indicators, and to determine the potential of GIS tools and selected open data sources for automating this process. GIS was used to represent data. The intermediate goal was to determine differences in the SFS solutions of two MFREs that were built with different technologies and urban layouts in the last 70 years in the city of Olsztyn (Poland). Methods: An empirical study was conducted with the use of qualitative and quantitative methods based on a review of the literature, the results of a resident survey, and an analysis of spatial data in ArcGIS and QGIS software. Results: The residents’ needs for SFS in MFREs were identified. A list of 26 SFS indicators and their values (on a 3-point scale) was developed to assess multi-family housing. The applicability of GIS software and spatial data from the national spatial data infrastructure (NSDI) and other sources was assessed in the process. The research method was tested to reveal differences in SFS solutions in the compared MFREs.

Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis

This study is twofold: (i) whether there is a significant difference between Chinese learners’ first language (L1) proficiency and second language (L2) performance in comprehension of certain multiply self-embedded complex English sentences such as (1) Tom knows that the fact that taking good care of himself is necessary surprises the boy; (ii) whether there is a relationship between Chinese learners’ L1 proficiency and L2 processing strategies in the use of syntactic and/or semantic information in the comprehension performance. Two groups of advanced Chinese learners of English differing in Chinese proficiency (i.e. fifteen high Chinese proficiency (HCP) and fifteen low Chinese proficiency (LCP) learners of English) and one control group participated in the off-line experiment to answer questions to English sentences based on comprehension accuracy. The results show that there are no significant accuracy differences between HCP learners and LCP learners in the complex English sentence comprehension, suggesting that L1 proficiency play no significant role in Chinese learners’ complex English sentence comprehension. When doing a complex-rating task where participants rated complexity of the self-embedded complex English sentences based on a five-point scale, the results show that the LCP Chinese learners of English, like the control native English speakers, were able to use both syntactic and semantic information. However, the HCP Chinese learners of English relied more on semantic information than syntactic cues, indicating that L1 proficiency may influence Chinese learners’ processing strategy in comprehension of the complex English sentences.

Research in Construction Grammar assumes no strict separation between syntax and the lexicon. However, recent work by Goldberg (1995, 2006) shows that there is indeed a separation between lexical entries and grammatical constructions, including constraints regulating the fusion of grammatical constructions with verbs. This paper argues that Goldberg’s characterization of the interactions between lexical entries and grammatical constructions faces some of the same difficulties as the interactions between lexical entries and transformational rules in the Chomskyan framework (Chomsky, 1965, 1981, 1995). Drawing on a variety of corpus data this paper presents specific proposals that should be considered in order to arrive at a solution that overcomes difficulties inherent to Goldberg’s approach. Based on a discussion of the concepts of analogy, collocational restrictions, frequency, and productivity this paper proposes to encode different types of semantic, pragmatic, and syntactic information in such a way that it is possible to account for a given utterance from a comprehension perspective, as well as a production perspective.

New imaging mode has been brought up for collecting multiple scenes in one pass, as is implemented on World View-II. This greatly helps for acquiring high spatial resolution images that cover urban areas, and is to be adopted in the coming Chinese satellites. This paper is to discuss the mosaic characteristic and propose a mosaic line generation method by integrating correlation and the road information. The mosaic line is formed by linking the unique mosaic point on each line restricted within the road. We position the starting point by connectivity analysis of the road lines, and then locate the adjacent point along the road with connectivity analysis. A weighed vector, combining correlation and distance to centre of the road, is used to pick the best point. The points are located on the road unless it is unavoidable, for example, the road ends or the line touches edge of the image. This method provides instant mosaic line generation for urban areas with road information available in most cases. By resorting to the road, the mosaic line is more applicable since many problems for mosaic of high spatial resolution images are solved, for example, tilting of the buildings, the shadows, motions of the vehicles etc. Experiments have been done with WV-II images and gained favorable results.