Base Phrase Research Articles

Language at a glance: How our brains grasp linguistic structure from parallel visual input.

Human brains grasp the gists of visual scenes from a single glance, but to what extent is this possible for language? While we typically think of language in terms of sequential speech, our everyday experience involves numerous rapidly flashing written notifications, which we understand instantly. What do our brains detect in the first few hundred milliseconds after seeing such a stimulus? We flashed short sentences during magnetoencephalography measurement, revealing sentence-sensitive neural activity in left temporal cortex within 130 milliseconds. These signals emerged for subject-verb-object sentences regardless of grammatical or semantic well-formedness, suggesting that at-a-glance language comprehension begins by detecting basic phrase structure, independent of meaning or other grammatical details. Our findings unveil one aspect of how our brains process information rapidly in today's visually saturated world.

Improvements in Similarity Measurement Method for Emotion Classification of Japanese Sentences

This study proposes the following three improvements to address the shortfalls of existing methods for emotion classification based on similarity measures between Japanese sentences. (1) The proposed method utilizes, as a similarity measure, an automatic evaluation metric for machine translation based on correlation considering global word order. Similarity measures used in existing methods are too strict or lax in their consideration of the order of words during the processing of Japanese sentences. They fail to incorporate the fact that Japanese exhibits a higher degree of freedom in word order compared to English. (2) Rather than word-level alignment, the proposed method aligns two sentences at the level of a base phrase constituted by one or more content words followed by zero or more function words. Word-level alignment, used by existing methods, may cause inappropriate matching, because it independently processes content words and function words which constitute a base phrase. (3) The proposed method integrates, using linear interpolation, similarity scores by the alignment of word-level and those of base phrase-level. Integration can avoid data sparsity, namely a problem that a similarity score between two sentences becomes zero, occurring when base phrase-level alignment is made. The experimental results found that (1) the proposed method demonstrated significantly higher classification accuracy than existing alternatives; (2) the base phrase-level alignment was effective because the alignment exhibited higher classification accuracy than the word-level alignment; and (3) integration of the word-level and base phrase-level alignments was valid because the integrated method outperformed the base phrase-level alignment. Key Words: Emotion classification, Similarity measure, Japanese, Word order, Base phrase, Linear interpolation

Chinese Natural Language Processing: From Text Categorization to Machine Translation

Abstract The level and volume of automatic computerized processing of linguistic information has become one of the most important criteria for measuring whether a country has entered the information society. The study begins with statistical linguistics and aims to process complicated Chinese information. In this paper, after establishing the word database of the Chinese language, the language model is smoothed and compressed, the Chinese character information and Chinese language information are extracted, and the processing of Chinese grammar and Chinese semantic information is emphasized. Among them, Chinese grammar processing includes Chinese word analysis and basic phrase analysis based on the maximum entropy model, and Chinese semantic processing includes Bayesian-based word sense disambiguation, semantic role labeling based on the conditional random field model, and thesaurus-based semantic similarity calculation method. In addition, SECTILE-based Chinese text categorization and statistical linguistics-based machine translation methods are explored and tested for their effectiveness in Chinese natural language processing. The results show that the total average check accuracy and check the completeness of Chinese text are 78.65% and 72.24%, respectively, and the BLEU values of the translation methods are improved by [1.62,3.73] and [0.93,5.01] compared with the Baseline method, which is able to process the Chinese information accurately. The research plays an important role in the process of information processing based on Chinese language processing.

A systematic review of infopreneurship’s opportunities and challenges

In this study, the literature on the opportunities and challenges for infopreneurship was compiled using the systematic literature review (SLR) method. The study's specific goal was to look at the infopreneurship opportunities that are available and the obstacles that LIS graduates must overcome in order to take use of them. In a methodical manner, relevant research publications from journals were downloaded, reviewed, and interpreted. The researchers used numerous research strategies, such as keyword and phrase searches, to assure the precision of the study papers collected. Because basic phrase and key word searches produced substantial results, advanced search tactics like truncation and Boolean operators were not employed in this investigation. The researchers employed multiple databases, including emerald, research4life, Ebsco host, FullfreePDF, and Google scholar, with base search serving as the primary search engine. The study found out several opportunities that existed on infopreneurship, some of which include Internet blogging, software and hardware installation, graphic design, using information, customized information, facilitating access to information, repackaging information, increasing the flow of information, digitization and internetworking. The study further found a number of challenges that make it less likely for infopreneurs to seize and exploit these opportunities such as financial constraints, the price of necessary equipment, a lack of infrastructure, the legal registration process, graduates' lack of skills, and poor planning. The study concludes that information entrepreneurship is essential to any nation's economic sector. The study suggests that by reviewing LIS curricula we can increase student understanding of infopreneurship practice.

Students’ speech development by teaching phraseological units

In this article, the lexical-phraseological works on the cultivation of speech of Primary School students, work on vocabulary, the methods of lexical-phraseological analysis, the specific role and importance of teaching phraseological combinations to junior students are highlighted Base words and phrases: Phraseological combinations, lexical-phraseological analysis, stable combinations, aphorisms, speech-growing techniques.In accordance with the laws of the Republic of Uzbekistan “on the state language”, “on education”and “national program of training of personnel”, the educational system of the Republic of Uzbekistan sets new requirements for continuous education. In particular, the main purpose of teaching native language in primary education is aimed at the development of a person who can freely and effectively express his/her opinion in written and oral form, consciously mastering the rules of the Uzbek language, as well as making appropriate and correct use of theoretical knowledge, developed speech culture.

LANGUAGE CHARACTERISTICS OF BEAUTY PRODUCT ADVERTISEMENTS

The objectives of this study are to describe the linguistics characteristics of beauty product advertisements, to investigate the influence of buyer behaviors towards the beauty products advertisements. The data for the study were phrases, and sentences drawn from selected beauty products advertisemets. The linguistics characteristics of language are analysed based on Chaer theory and the influence of the consumer behaviors is based on Rapp and Collins theory. The results showed that (1) Based on the sentence,which is devided into(a) naturalness and unnatural (b) Number of sentence forming clauses (c) active and passive sentence. (2) based on phrase, which is divided into (a) basic phrase and derivative phrase, (b) phrase function,which divided into nominal phrase, verbal phrase, adjective phrase, and prepositional phrase.(3) Based on language functionm which divided into (a) evocative, informative, persuasive and powered language. Based on the result of study, it can be seen that the number of no advertisement factors presentations on on consumer buying behavior was 62,6 %, while the advertising language factors was only 37,4 %. This means that the language of beauty product advertising does not have a strong relationship with consumer buying behavior. The language is only able to trigger consumer to look for more detailed explanation and find more information from more trusted sources. Therefore, the language of advertising has a relationship that is not strong and indirect with buying behavior by consumers. Keywords: Linguistic Characteristics, Consumer Behavior, Beauty Products, Advertisement.

Simulation of English part-of-speech recognition based on machine learning prediction algorithm

English part-of-speech intelligent recognition is the scientific and technological basis for the development of intelligent speech systems. The difficulty in the current English speech recognition system lies in the recognition of English parts of speech. In order to improve the effect of English part-of-speech recognition, this study builds the language rules and morphological models of English morphological forms based on machine learning algorithms. Moreover, this study proposes a stemming extraction algorithm and a syllable division algorithm based on English characteristic rules. By studying basic phrases in English, this study analyzes the compositional structure of phrases, and determines the basic phrase structure and composition rules of English such as noun, verb, and adjective. In addition, this research studies the basic English phrase recognition algorithm based on the rule method and the analysis of basic phrase ambiguity resolution. Finally, this study designs a control experiment to analyze the performance of the algorithm proposed in this paper model and confirm the classification algorithm. The research results show that the algorithm proposed in this paper has a certain practical effect.

CONGLOMERATE MANAGEMENT OF ENTERPRISE AS SOCIO-ECONOMIC SYSTEM

In modern conditions of political, economic, and social globalization, it is difficult to overestimate for Ukraine the need for research and introduction of new forms of enterprise management. World tendencies of the interaction of business elements are characterized by decentralized integration, in which, preserving their legal and industrial identity, small and medium business is united in economic communities, trying to withstand large monopolistic associations. The modern business environment in the dual model at one pole is represented by corporations, TNCs, MNCs, and at the other pole – by free entrepreneurs (freelancers), the number of which is steadily growing. The contradictions and shortcomings of both poles are harmonized by the intra-national integration of economic interests. New business models of economic interaction arise that require new models and forms of management, require the creation of new, adaptive formats of business interaction that involves the mandatory inclusion of standards for sustainable development (economic, environmental, and social mechanism). Such a new form of management, in our view, can be an innovative model of conglomerate management, which implies not only conglomerate mergers and acquisitions of corporations but also new mechanisms for interaction of small business structures of different economic and sectoral orientation. The terminology and the theoretical basis for studying the conglomerate as a socio-economic system that includes the basic phrase “socio-economic system” were defined. Such a form of organization of socio-economic systems, which would consolidate high competitive advantages, innovative orientation, and significant expansive potential, is proposed. On the basis of generalization of the corresponding theoretical provisions and revealed peculiarities of the formation of spatially localized systems, we assumed that emerging as local “points” of economic growth zones, individual elements or subsystems of enterprises are transformed into territorial production complexes (hereinafter abbreviated as TPC), and then – by implementing the missing infrastructure and institutional components into a variety of cluster forms, into conglomerates. As a result of the research, the following scientific substantiations and conclusions are formulated: 1. For the first time, the term “conglomerate management” was proposed as the aggregate interaction of enterprises united into a spatially localized socio-economic system and a methodological approach to managing such complex structures. 2. A terminology for investigating the purposeful transformation of society and the economy into an additive socioeconomic system is developed. Such studies are especially relevant for the modern domestic economy, which contains controversial characteristics of the deterministic and nondeterministic economic model, as well as poorly predicted agglomerate and synergistic effects. 3. A model for organizing and managing conglomerates of a new type is formed. The architecture of the conglomerate enterprise management model as an element of a socio-economic system with an innovative basis as a priority for the dynamic development of a conglomerate is proposed. 4. We also find it economically sound to create a model of developing the strategic potential of an enterprise as an open socio-economic system, to use the mathematical apparatus of studying models of the fractal type that describes the evolution of a modelled system of fractal objects. 5. A parametric characteristic of the formation of a deterministic model of conglomerate control of a fractal type as a socio-economic system was developed and brought to the form of an economic-mathematical model.

The Electrophysiology of Basic Phrase Building

A defining trait of linguistic competence is the ability to combine elements into increasingly complex structures to denote, and to comprehend, a potentially infinite number of meanings. Recent magnetoencephalography (MEG) work has investigated these processes by comparing the response to nouns in combinatorial (blue car) and non-combinatorial (rnsh car) contexts. In the current study we extended this paradigm using electroencephalography (EEG) to dissociate the role of semantic content from phonological well-formedness (yerl car). We used event-related potential (ERP) recordings in order to better relate the observed neurophysiological correlates of basic combinatorial operations to prior ERP work on comprehension. We found that nouns in combinatorial contexts (blue car) elicited a greater centro-parietal negativity between 180-400ms, independent of the phonological well-formedness of the context word. We discuss the potential relationship between this ‘combinatorial’ effect and classic N400 effects. We also report preliminary evidence for an early anterior negative deflection immediately preceding the critical noun in combinatorial contexts, which we tentatively interpret as an electrophysiological reflex of syntactic structure initialization.

Minding the gap: An experimental assessment of musical segmentation models.

Music and language are parallel in many respects (for overviews, see Besson & Friederici, 1998; Patel, 2010), including their origins and historical development (Wallin, Merker, & Brown, 2000), patterns of development across the life span (Drake, 1998), and elements of cognitive processing (Swinney & Love, 1998). Although music and language are different types of stimuli and utilize different cognitive operations (Patel, 1998; Peretz, 1993; Peretz et al., 1994), they appear to share several structural properties (Blacking, 1973; Krumhansl, 1998; Lerdahl & Jackendoff, 1983; Raffman, 1993; Sloboda, 1985). For example, in music and in language, structural rules specify how combinations of smaller units form larger structural frameworks (e.g., notes forming melodies, words forming sentences). However, structural properties of music are more ambiguous and fluid than are structural properties of language (Jackendoff & Lerdahl, 1981; Jescheniak, Hahne, & Friederici, 1998), and the specific structural rules by which a listener might segment a larger musical stimulus into smaller meaningful units are not entirely clear. To better understand the process of musical segmentation, the experiments presented here provide an empirical evaluation of structural rules suggested by two different models of music cognition: the perceived phrase structure model (PPS; Temperley, 2001) and the generative structural grammar of music model (GSGM; Lerdahl & Jackendoff, 1983).Based in part on the notion of generative structural grammars in language (Chomsky, 1965, 1968, 1957/2002; Yngve, 1960), analogous generative structural grammars have been proposed for music (Deutsch & Feroe, 1981; Lerdahl & Jackendoff, 1983; Tenney & Polansky, 1980; Winograd, 1968). The unit of the motif, which can be as small as two notes, is important both structurally and psychologically (see Jackendoff & Lerdahl, 1981; Zbikowski, 1999). Generative grammars combine these motifs into basic phrase units to form the lower level of a hierarchical structure (see Gregory, 1978; Johnson, 1965; Rosenberg, 1968, for empirical support for the functional use of such a structural grammar). Given the fundamental importance of the phrase, and the importance of understanding basic units before proceeding to more complicated hierarchies based on combinations of those basic units, the focus here will be upon segmentation within a local context (i.e., on phrase-level context rather than on extended and hierarchically organized context). To wit, we assessed how participants determine phrase boundaries in musical stimuli and segment those musical stimuli into meaningful parts. There is a paucity of literature assessing segmentation models in diverse (i.e., experienced and inexperienced listener) human (i.e., nonmachine) populations. The relative novelty of our study lies within the populations that we studied and the hypotheses that we tested. Specifically, we studied humans with a range of musical experience and compared their segmentation responses with theoretical segmentation points generated by two prominent musical segmentation theories.The GSGM model is a prominent and influential theory that proposes several grouping preference rules for determining the locations of local context phrase boundaries in music (see Table 1). The GSGM model suggests that when one or more of these grouping rules is fulfilled, then perceived segmentation (i.e., perception of a phrase boundary) typically occurs. The rules are not mutually exclusive, and different rules can be combined to create different strengths or levels of segmentation. Deliege (1987) examined the extent to which perceptual grouping corresponded with the local context segmentation rules of GSGM for a group of listeners. Deliege presented short musical excerpts, and musician or nonmusician participants indicated the point at which they heard natural breaks in the music by drawing a vertical line in a row of horizontally oriented dots in which successive dots represented successive notes. …

Galant Recitative Schemas

Italianate recitative of the eighteenth and early nineteenth centuries constituted a distinct musical language that coexisted with the familiar style of common practice music. In this article we propose a theory of that language (“galant recitative”), which we characterize in terms of a core vocabulary of fifteen melodic formulas. Appendix 1 presents a roster of these formulas, which captures a unique fingerprint of recitative, distinct from the comparable inventory of homophonic phrase schemas in Robert Gjerdingen’s Music in the Galant Style. These recitative schemas are defined in terms of characteristic melodic profiles, harmonic contexts, formal roles, and expressive associations. One representative schema, the “O cielo,” is used to demonstrate the musical features and range of variation characteristic of all recitative schemas. The formal associations of the schemas allow us to describe the basic phrase syntax of recitative, grouping the schemas into three broad formal roles: initiatory, medial, and closing. Appendix 2 demonstrates this formal model by analyzing the dramatic climax of Leonardo Vinci and Pietro Metastasio’s opera Artaserse (1730). Additionally, some schemas have focused semantic connotations and are used to mark such utterance types as questions and witticisms. The article concludes with an analysis of scenes from act 1 of Mozart’s Così fan tutte, which shows how sensitivity to manipulations of galant recitative schemas can reveal subtle effects of characterization and dramatic continuity.

Sequential Linefeed Insertion into Lecture Transcriptions for Real‐Time Captioning

SUMMARYTo generate readable captions for Japanese spoken monologues such as lectures in real time, it is necessary to sequentially display captions that have proper linefeeds inserted. This paper proposes a technique for sequentially inserting proper linefeeds into a lecture transcript whenever a bunsetsu, which is a linguistic unit shorter than a sentence in Japanese and that roughly corresponds to a basic phrase in English, is identified. Under the assumption that linefeeds are inserted at bunsetsu boundaries, this technique can reduce the delay time of captioning to the utmost possible. This technique statistically judges whether or not a linefeed should be inserted into each bunsetsu boundary by using the information that is available at the time. We conducted experiments on linefeed insertion using a Japanese lecture corpus. The experimental results confirmed that our method, which is a bunsetsu‐based linefeed insertion method, was almost as accurate as the sentence‐based linefeed insertion method. In addition, we conducted comparative evaluations using four baseline methods. The results confirmed that our method could insert linefeeds more accurately than the simple methods that are thought to have the same delay time as our method.

Sequential Linefeed Insertion into Lecture Transcription for Real-time Captioning

To generate readable captions for Japanese spoken monologue such as lectures in real time, it is necessary to sequentially display the caption into which proper linefeeds are inserted. This paper proposes a technique for sequentially inserting proper linefeeds into the lecture transcript whenever a bunsetsu, which is a linguistic unit shorter than a sentence in Japanese that roughly corresponds to a basic phrase in English, is identified. Assuming that linefeeds are inserted into bunsetsu boundaries, the technique can make the delay time of captioning shortest. The technique statistically judges whether or not a linefeed should be inserted into each bunsetsu boundary by using the information which is available at the time. We conducted experiments on linefeed insertion using a Japanese lecture corpus. From the experimental results, we confirmed that our method, which is bunsetsu-based linefeed insertion method, had almost as much accuracy as the sentence-based linefeed insertion method. In addition, we conducted the comparative evaluations with four baseline methods. As the results, we confirmed that our method could insert linefeeds more properly than the simple methods which are thought to have as same delay time as our method.

Efficient Classification with Conjunctive Features

This paper proposes a method that speeds up a classifier trained with many conjunctive features: combinations of (primitive) features. The key idea is to precompute as partial results the weights of primitive feature vectors that represent fundamental classification problems and appear frequently in the target task. A prefix tree (trie) compactly stores the primitive feature vectors with their weights, and it enables the classifier to find for a given feature vector its longest prefix feature vector whose weight has already been computed. Experimental results on base phrase chunking and dependency parsing demonstrated that our method speeded up the SVM and LLM classifiers by a factor of 1.8 to 10.6.

Utilizing Various Natural Language Processing Techniques for Biomedical Interaction Extraction

The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NO-INTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.

Diachronic Changes of Modifier-Head Phrases

Basic phrase types remain constant throughout the history of the Chinese language. However, functions of each phrase type went through certain changes that were not yet adequately discussed. This paper explores diachronic changes of functions of the modifier-head phrase, which is the commonest phrase type. Remarkable changes include the following four types: (1)The way of expressing similarity only through word sequence of the phrase disappeared; (2) Monosyllabic verbs became highly restricted to appear on the modifier position; (3) Using a verb as a modifier to express the semantic relation “action+patient”, ‘de （的）’ became necessary except for some compound words; (4)Some elements denoting interpersonal information on the modifier position disappeared.

Semi-Markov Conditional Random Fields のための損失関数スムージング

Linear-chain conditional random fields are a state-of-the-art machine learner for sequential labeling tasks. Altun investigated various loss functions for linear-chain conditional random fields. Tsuboi introduced smoothing method between point-wise loss function and sequential loss function. Sarawagi proposed semi-markov conditional random fields in which variable length of observed tokens are regarded as one node in lattice function. We propose a smoothing method among several loss functions for semi-markov conditional random fields. We draw a comparison among the loss functions and smoothing rate settings in base phrase chunking and named entity recognition tasks.

Extracting relation information from text documents by exploring various types of knowledge

Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using support vector machines. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while current commonly used features from full parsing give limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. This indicates that a cheap and robust solution in relation extraction can be achieved without decreasing too much in performance. We also demonstrate how semantic information such as WordNet, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE benchmark corpora shows that effective incorporation of diverse features enables our system outperform previously best-reported systems. It also shows that our feature-based system significantly outperforms tree kernel-based systems. This suggests that current tree kernels fail to effectively explore structured syntactic information in relation extraction.

Incorporating linguistic structure into maximum entropy language models

In statistical language models, how to integrate diverse linguistic knowledge in a general framework for long-distance dependencies is a challenging issue. In this paper, an improved language model incorporating linguistic structure into maximum entropy framework is presented. The proposed model combines trigram with the structure knowledge of base phrase in which trigram is used to capture the local relation between words, while the structure knowledge of base phrase is considered to represent the long-distance relations between syntactical structures. The knowledge of syntax, semantics and vocabulary is integrated into the maximum entropy framework. Experimental results show that the proposed model improves by 24% for language model perplexity and increases about 3% for sign language recognition rate compared with the trigram model.

Memory-based shallow parsing

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement.