Word Database Research Articles

Research of Chinese Word Knowledge Graph Based on SLPA Algorithm

The different semantics of Chinese words will make a variety of relations between Chinese words. In the known relationship, synonymy, antisense, hypernym, etc. are the most common relationship. Because of the intricate relationship between Chinese words, so that words and other words form a series of knowledge network, which contains a variety of useful information. Based on these knowledge networks, this paper establishes the knowledge graph of Chinese words and analyzes the inherent characteristics of Chinese words. Establish the semantic database of words, and improve the semantic library structure, including the relationship between the number of semantic library words and words (synonymy, hypernym, etc). Based on the semantic database, the semantic network is formed, and community discovery is carried out for the collection of synonyms in the semantic network, and the community properties are extracted to form a complete semantic relation network.

デザイン用語という専門日本語のコーパス構築の試み

デザイン用語という専門日本語のコーパス構築の試み

Frail or hale: Skeletal frailty indices in Medieval London skeletons.

To broaden bioarchaeological applicability of skeletal frailty indices (SFIs) and increase sample size, we propose indices with fewer biomarkers (2–11 non-metric biomarkers) and compare these reduced biomarker SFIs to the original metric/non-metric 13-biomarker SFI. From the 2-11-biomarker SFIs, we choose the index with the fewest biomarkers (6-biomarker SFI), which still maintains the statistical robusticity of a 13-biomarker SFI, and apply this index to the same Medieval monastic and nonmonastic populations, albeit with an increased sample size. For this increased monastic and nonmonastic sample, we also propose and implement a 4-biomarker SFI, comprised of biomarkers from each of four stressor categories, and compare these SFI distributions with those of the non-metric biomarker SFIs. From the Museum of London WORD database, we tabulate multiple SFIs (2- to 13-biomarkers) for Medieval monastic and nonmonastic samples (N = 134). We evaluate associations between these ten non-metric SFIs and the 13-biomarker SFI using Spearman’s correlation coefficients. Subsequently, we test non-metric 6-biomarker and 4-biomarker SFI distributions for associations with cemetery, age, and sex using Analysis of Variance/Covariance (ANOVA/ANCOVA) on larger samples from the monastic and nonmonastic cemeteries (N = 517). For Medieval samples, Spearman’s correlation coefficients show a significant association between the 13-biomarker SFI and all non-metric SFIs. Utilizing a 6-biomarker and parsimonious 4-biomarker SFI, we increase the nonmonastic and monastic samples and demonstrate significant lifestyle and sex differences in frailty that were not observed in the original, smaller sample. Results from the 6-biomarker and parsimonious 4-biomarker SFIs generally indicate similarities in means, explained variation (R2), and associated P-values (ANOVA/ANCOVA) within and between nonmonastic and monastic samples. We show that non-metric reduced biomarker SFIs provide alternative indices for application to other bioarchaeological collections. These findings suggest that a SFI, comprised of six or more non-metric biomarkers available for the specific sample, may have greater applicability than, but comparable statistical characteristics to, the originally proposed 13-biomarker SFI.

Valence, arousal, familiarity, concreteness, and imageability ratings for 292 two-character Chinese nouns in Cantonese speakers in Hong Kong.

Words are frequently used as stimuli in cognitive psychology experiments, for example, in recognition memory studies. In these experiments, it is often desirable to control for the words’ psycholinguistic properties because differences in such properties across experimental conditions might introduce undesirable confounds. In order to avoid confounds, studies typically check to see if various affective and lexico-semantic properties are matched across experimental conditions, and so databases that contain values for these properties are needed. While word ratings for these variables exist in English and other European languages, ratings for Chinese words are not comprehensive. In particular, while ratings for single characters exist, ratings for two-character words—which often have different meanings than their constituent characters, are scarce. In this study, ratings for 292 two-character Chinese nouns were obtained from Cantonese speakers in Hong Kong. Affective variables, including valence and arousal, and lexico-semantic variables, including familiarity, concreteness, and imageability, were rated in the study. The words were selected from a film subtitle database containing word frequency information that could be extracted and listed alongside the resulting ratings. Overall, the subjective ratings showed good reliability across all rated dimensions, as well as good reliability within and between the different groups of participants who each rated a subset of the words. Moreover, several well-established relationships between the variables found consistently in other languages were also observed in this study, demonstrating that the ratings are valid. The resulting word database can be used in studies where control for the above psycholinguistic variables is critical to the research design.

Comprehensive collection for Arabic characters and numbers written by hand

An Optical Character Recognition system for Arabic language should recognize Arabic handwritten words. However, it is difficult to find a freely accessible and comprehensive database of all Arabic words that can be employed for this purpose. Therefore, it is more efficient to divide the Arabic words into sub-words or characters. As there is no comprehensive Arabic handwritten character database that is accessible free of charge, interested researchers can utilize the database developed as a part of this work in recognition system training and output testing.In the present paper, a database is presented containing scanned images of 700 Arabic handwritten characters, Hindi numbers used in Arabic countries, and some special characters utilized in Arabic alphabet, along with their different positions (e.g., standalone, initial, medial and terminal), different sizes, styles and font colors. The aim is to provide sufficient samples for all character shapes for software training, resulting in greater accuracy in the recognition phase.These forms were filled by students of the Applied Sciences College, University of Technology, Baghdad, Iraq and were scanned at the 200, 300, and 600 dpi resolution. A graphical user interface (GUI) software environment is employed to make the manipulation of the created database easier, and provide many image processing functions that are allowed to be built the database easier

Applying Z-Curve Technique to Compute Skyline Set in Multi Criteria Decision Making System

The skyline queries are the best tools to be used in distributed multi criteria decision making of web based applications for user commendations. However, as the Data dimensions are increasing size of dominance set and skyline set is also increasing. Increasing dimensionality becomes the major problem with real word databases. In skyline computation major cost depends on finding dominance tests between high dimensional objects and the order in which they are accessing. Space filling Z-curve is the best suitable way to address the challenges in skyline computation. In this proposed work, we incorporated Z-curve with optimized skyline boundary detection algorithm to effective access and early pruning. In this paper efficient hybrid index structure was proposed which takes the advantage of sorting and partition approaches to improve the storage and search efficiency. Experimental results show that our propose approach is better than the previous static skyline computation techniques in terms of searching and finding skyline set.

E-READING II: words database for reading by students from Basic Education II.

To develop a database of words of high, medium and low frequency in reading for Basic Education II. The words were taken from the teaching material for Portuguese Language, used by the teaching network of the State of São Paulo in the 6th to the 9th year of Basic Education. Only nouns were selected. The frequency with which each word occurred was recorded and a single database was created. In order to classify the words as of high, medium and low frequency, the decision was taken to work with the distribution terciles, mean frequency and the cutoff point of the terciles. In order to ascertain whether the words of high, medium and low frequency corresponded to this classification, 224 students were assessed: G1 (6th year, n= 61); G2 (7th year, n= 44); G3 (8th year, n= 65); and G4 (9th year, n= 54). The lists of words were presented to the students for reading out loud, in two sessions: 1st) words of high and medium frequency and 2nd) words of low-frequency. Words which encompassed the exclusion criteria, or which caused discomfort or joking on the part of the students, were excluded. The word database was made up of 1659 words and was titled 'E - LEITURA II' ('E-READING II', in English). The E-LEITURA II database is a useful resource for the professionals, as it provides a database which can be used for research, educational and clinical purposes among students of Basic Education II. The professional can choose the words according to her objectives and criteria for elaborating evaluation or intervention procedures involving reading.

Encoding emotion in Chinese: a database of Chinese emotion words with information of emotion type, intensity, and valence

Despite the increasing interest in emotion and sentiment analysis in Chinese text, the field lacks reliable, normative ratings of the emotional content and valence of Chinese emotion words. This paper reports the first large-scale survey of average language users’ judgment of perceived emotion type (e.g., anger, happiness), emotional intensity, and valence (e.g., positive, negative) of Chinese emotion words. The results of the survey reveal significant differences from previously proposed Chinese emotion lexicons, which mostly relied on a few researchers’ judgment or automatic annotation. Furthermore, the current study also explores the issue of lexical variation across different Chinese varieties with a comparison of emotion word perception by Chinese speakers from three different areas (Mainland China, Hong Kong, and Singapore). The emotion lexicons constructed in the current study will serve as an important reference for future research on emotion and language, including (but not limited to) topics related to sentiment detection and analysis, perception of affective language, and cross-regional lexical and semantic variation in Chinese.

Revisiting articulatory positions of Japanese vowels as a function of duration on the basis of analysis of large-scale speech corpora

The purpose of this study is to illustrate certain features of the place of articulation of Japanese vowels on the basis of formant frequency. For this purpose, two types of relationship between vowel duration and formant frequency were examined. The first of these relationships was the correlation between speaking rate and formant frequency. Speech materials are obtained from two large-scale speech corpora: Electrotechnical Laboratory Spoken Word database (ETL-WD) and The corpus of spontaneous Japanese (CSJ). The results of analyses showed that as speaking rate slows, articulations get more distinct: open vowels increase in openness, close vowels increase in closeness, front vowels increase in frontness, and back vowels increase in backness. The second relationship was between formant frequencies and vowel length. The result of this analysis showed that place of articulation is more distinct in long vowels than in short vowels. The exception was the back vowel /u/, all of which did not show greater backn...

Tamil Speech Word Recognition System with Aid of ANFIS and Dynamic Time Warping (DTW)

It is unfortunate that though the extant Tamil speech word recognition techniques have come out successful in detecting speech words from the speech word database by means of MFCC (Mel Frequency Cepstral Coefficients) features and FFBNN (Feed Forward Back Propagation Neural Network), they seem to have failed miserably to come up to expectations by generating less than desired outcomes of recital in terms of precision. Hence we have proudly launched, through this document, an innovative Tamil speech recognition technique to address the challenge by making use of novel features with ANFIS (Adaptive Neuro Fuzzy Inference System) based recognition method. Thus, at the outset, preprocessing is performed to cut down the noise in the input speech signals. Thereafter, feature vectors are mined from the preprocessed speech signals and furnished to the ANFIS. The epoch making technique is home to three novel features such as Energy Entropy, Short Time Energy and Zero Crossing Rate which are mined from the Tamil speech word signals and subjected to the word recognition procedure, in which, the ANFIS system is well guided by the features from feature mining task and the recognition efficiency is authenticated by using a set of test speech words. In the course of the testing stage, with a view to achieving exact outcomes, the dynamic time warping is estimated by means of the guidance and test word feature values. The performance outcomes illustrate the fact that the innovative Tamil speech word recognition technique has been able to achieve amazing efficiency in recognizing the input Tamil speech words, in addition to yielding higher levels of achievement in terms of precision. Moreover, the accomplishment of the well-conceived recognition technique is assessed and contrasted with the modern Tamil speech word recognition techniques.

Discussion paper: The importance of psycholinguistic databases for research with children: The case of HelexKids, a Greek word database for children

Psycholinguistic research with children requires child-specific word databases, as adult word databases are not appropriate for a number of reasons. While some child-specific word databases do exist, they are not available in all languages. This article describes the development of a child-specific word frequency database in Greek. This database, named HelexKids, provides standardised values for word frequency, dispersion and contextual diversity. HelexKids is freely available online for all researchers and educators to use. The advantages of using child-specific databases in research with children and future directions for further development of this database are discussed.

In Skinner's Early Footsteps: Analyzing Verbal Behavior in Large Published Corpora

Using data from large databases of words that listeners have rated in terms of how positively or negatively they respond to them, we present a mode of analysis that reveals regularities across several languages. Specifically, a normalized measure of bias in emotional valence shows that the degree to which positively-valenced words predominate in a language is a reliable function of absolute valence intensity. This measure supports the comparison of corpora in which valence has been measured in different ways and can serve as a baseline for evaluating the role of interactive variables. We also suggest a way to apply the measure to characterize the verbal behavior of individuals. Throughout we show how the general topic is amenable to the functional perspective that behavior analysts apply to verbal behavior, which creates an opportunity for behavior analysts to contribute to interdisciplinary inquiries into verbal phenomena.

I saw this somewhere else: The Spanish Ambiguous Words (SAW) database

The present paper describes the Spanish Ambiguous Words (SAW) database, which comprises 210 words (133 polysemous and 77 homographs). Three-hundred and fifteen Spanish university students took part in the study on which SAW is based. First, subjective word meanings and senses were collected by means of a meaning retrieval task. Two judges then assigned participants’ responses to different categories of meaning according to lexicographical and statistical criteria. Results showed that, while there was a relatively high relationship between the number of senses included in the dictionary and those provided by participants (r=.62), regression analyses on lexical decision and naming times revealed that participants’ number of senses had significant predictive power, whereas those from the dictionary did not. This indicates that normative data from participants seem to better reflect the psychological reality of word senses. Hence, the SAW database constitutes a useful tool for future research into ambiguous word learning and recognition in Spanish.

Semantic ambiguity norms for 530 Spanish words

ABSTRACTThis study presents semantic ambiguity norms for 530 Spanish words. Two subjective measures of semantic ambiguity and two subjective measures of relatedness of ambiguous word meanings were collected. In addition, two objective measures of semantic ambiguity were included. Furthermore, subjective ratings were obtained for some relevant lexicosemantic variables, such as concreteness, familiarity, emotional valence, arousal, and age of acquisition. In sum, the database overcomes some of the limitations of the published databases of Spanish ambiguous words; in particular, the scarcity of measures of ambiguity, the lack of relatedness of ambiguous word meanings measures, and the absence of a set of unambiguous words. Thus, it will be very helpful for researchers interested in exploring semantic ambiguity as well as for those using semantic ambiguous words to study language processing in clinical populations.

An English Word Database of EMOtional TErms (EMOTE).

Research in the socio-emotional domain may require words for experimental settings rated on emotionally and socially relevant word characteristics (e.g., valence and desirability). In addition, cognitively relevant word characteristics (e.g., imagery) are important for research in the interface of emotion and cognition (e.g., emotional memory). To provide researchers with a corresponding word pool, the database of English EMOtional TErms (EMOTE) provides subjective ratings for 1287 nouns and 985 adjectives. Nouns and adjectives were rated on valence, arousal, emotionality, concreteness, imagery, familiarity, and clarity of meaning. In addition, adjectives were rated on control, desirability, and likeableness. EMOTE norms provide an easily accessible word pool for research in the socio-emotional domain. To illustrate the usefulness of this database, norms were linked to memorability scores from a word recognition task for EMOTE nouns. The database as well as future directions are discussed.

소셜네트워크서비스에 활용할 비표준어 한글 처리 방법 연구

특정한 관심이나 활동을 공유하는 관계망을 구축해주는 온라인 서비스인 소셜네트워크서비스(SNS), 자신의 관심사에 따라 자유롭게 글, 사진, 동영상 등을 올릴 수 있는 공간인 블로그(Blog) 등은 자신을 알리고 표현하는 사회현상으로 자리 매김하고 있다. 이러한 SNS나 블로그를 통해 사용자들이 자유롭게 표현한 글들을 분석하여 의미있는 정보와 가치, 그리고 패턴을 찾기 위한 텍스트 마이닝(Text Mining), 오피니언 마이닝(Opinion Mining), 의미 분석(Semantic Analysis) 등의 연구가 활발히 이루어지고 있다. 또한, 연구자들의 연구 효율을 보다 높이기 위하여 키워드 기반 연구들도 이루어져있다. 하지만 대부분의 연구들은 한글의 맞춤법에 많은 한계점을 나타내고 있다. 본 연구는 어근을 찾기 힘든 이상한 외계 언어, 무분별하게 표현되는 속어, 알기 힘든 한글 이모티콘 인터넷 언어, 마이닝 처리 과정에서 파악하기 어려운 단어들을 데이터베이스에 구축하여 데이터 사전 기반 마이닝 처리 기법의 한계를 극복하고자 한다. 특정 주제에 대한 주관적 견해로 구성된 블로그를 사례 분석 대상으로 연구를 진행하였으며 유니코드를 활용한 비표준어 추출은 텍스트 마이닝 처리에 유용함을 발견할 수 있었다. Social network services (SNS) that help to build relationship network and share a particular interest or activity freely according to their interests by posting comments, photos, videos,<TEX>${\ldots}$</TEX> on online communities such as blogs have adopted and developed widely as a social phenomenon. Several researches have been done to explore the pattern and valuable information in social networks data via text mining such as opinion mining and semantic analysis. For improving the efficiency of text mining, keyword-based approach have been applied but most of researchers argued the limitations of the rules of Korean orthography. This research aims to construct a database of non-standard Korean words which are difficulty in data mining such abbreviations, slangs, strange expressions, emoticons in order to improve the limitations in keyword-based text mining techniques. Based on the study of subjective opinions about specific topics on blogs, this research extracted non-standard words that were found useful in text mining process.

다중 언어로 작성된 문서 파일에 적용된 문자 인코딩 자동 인식 기법

Character encoding is a method for changing a document to a binary document file using the code table for storage in a computer. When people decode a binary document file in a computer to be read, they must know the code table applied to the file at the encoding stage in order to get the original document. Identifying the code table used for encoding the file is thus an essential part of decoding. In this paper, we propose a method for detecting the character code of the given binary document file automatically. The method uses many techniques to increase the detection rate, such as a character code range detection, escape character detection, character code characteristic detection, and commonly used word detection. The commonly used word detection method uses multiple word database, which means this method can achieve a much higher detection rate for multi-language files as compared with other methods. If the proportion of language is 20% less than in the document, the conventional method has about 50% encoding recognition. In the case of the proposed method, regardless of the proportion of language, there is up to 96% encoding recognition.

An improved SVM using predator prey optimization and Hooke-Jeeves method for speech recognition

For automatic speech recognition, speech signal is represented in terms of featureset. Size of the feature set is an important aspect as it affects recognition accuracy,computational time, memory requirement and complexity of the model. Supportvector machine has good application prospects for speech recognition; nevertheless,performance of support vector machine is affected by its parameters. In this researchwork, a hybrid optimization technique is proposed to improve the learning abilityof support vector machine and to select the most appropriate feature set. The hybridtechnique integrates predator-prey optimization and Hooke-Jeeves method. To dealwith mixed type of decision variables, binary predator-prey optimization technique hasalso been introduced. During the initial phase, search is performed by predator-preyoptimization and to further exploit the search, Hooke-Jeeves method is applied. Theproposed technique with support vector machine has been implemented to recognizeTI-46 isolated word database in clean as well as noisy conditions and self-recordedHindi numeral database. The experimental results obtained by proposed techniquewith support vector machine shows improved recognition rate. Furthermore ROCcurve is analysed to verify sensitivity and specificity of results obtained by proposedtechnique with support vector machine.

질의기반 사용자 프로파일을 이용하는 개인화 웹 검색

Search engines that rely on morphological matching of user query and web document content do not support individual interests. This research proposes a personalized web search scheme that returns the results that reflect the users' query intent and personal preferences. The performance of the personalized search depends on using an effective user profiling strategy to accurately capture the users' personal interests. In this study, the user profiles are the databases of topic words and customized weights based on the recent user queries and the frequency of topic words in click history. To determine the precise meaning of ambiguous queries and topic words, this strategy uses WordNet to calculate the semantic relatedness to words in the user profile. The experiments were conducted by installing a query expansion and re-ranking modules on the general web search systems. The results showed that this method has 92% precision and 82% recall in the top 10 search results, proving the enhanced performance.

Perception of Sung Speech in Bimodal Cochlear Implant Users.

Combined use of a hearing aid (HA) and cochlear implant (CI) has been shown to improve CI users’ speech and music performance. However, different hearing devices, test stimuli, and listening tasks may interact and obscure bimodal benefits. In this study, speech and music perception were measured in bimodal listeners for CI-only, HA-only, and CI + HA conditions, using the Sung Speech Corpus, a database of monosyllabic words produced at different fundamental frequencies. Sentence recognition was measured using sung speech in which pitch was held constant or varied across words, as well as for spoken speech. Melodic contour identification (MCI) was measured using sung speech in which the words were held constant or varied across notes. Results showed that sentence recognition was poorer with sung speech relative to spoken, with little difference between sung speech with a constant or variable pitch; mean performance was better with CI-only relative to HA-only, and best with CI + HA. MCI performance was better with constant words versus variable words; mean performance was better with HA-only than with CI-only and was best with CI + HA. Relative to CI-only, a strong bimodal benefit was observed for speech and music perception. Relative to the better ear, bimodal benefits remained strong for sentence recognition but were marginal for MCI. While variations in pitch and timbre may negatively affect CI users’ speech and music perception, bimodal listening may partially compensate for these deficits.