Use Of Linguistic Information Research Articles

The Effects of a Paraphrasing Intervention on Word Problem-Solving Accuracy of English Learners at Risk of Mathematic Disabilities

English learners (ELs) experience difficulty with mathematical problem solving because word problems require complex processes beyond basic math skills, such as the use of linguistic information, identifying relevant information, and constructing the appropriate problem statement. This study used a combined multiple baseline design and criterion changing design to assess the effectiveness of a paraphrasing intervention on the problem-solving performance for nine third-grade students who are ELs and at risk of mathematical disabilities (MD). Although the magnitude of the Tau-U effect sizes was in the small range, the visual analysis indicated that all students displayed increasing trends in problem-solving accuracy as a function of the paraphrasing intervention. The results were discussed in terms of providing continual support toward maintaining intervention outcomes.

The role of radical awareness in Chinese-as-a-second-language learners’ Chinese character reading development

ABSTRACTIn this paper, the role of radical awareness in Chinese character reading development among young Chinese-as-a-second-language (CSL) learners is evaluated in terms of a structural property of the writing system: the distribution of elementary, one-radical (i.e. one-element) simple characters as against majority, multi-radical compound characters. Radical awareness, that is, sensitivity to and use of linguistic information contained in compound characters’ constituent components (radicals), is known to facilitate character reading, but the specific structural property just mentioned, the distribution of simple and compound characters, has not been adequately investigated. In this study, 142 senior-primary-school CSL learners’ character reading ability and radical awareness were assessed (Time 1) and their compound character reading reassessed a year later (Time 2). Controlling for the effect of simple character reading, it was found that radical awareness uniquely predicted compound character reading at both Time 1 and Time 2, but not its development at Time 2 when previous competency at Time 1 was also controlled. A path model depicting the role of radical awareness in character reading development was constructed, and fit well with the data. The findings suggested that CSL learners would benefit from a systematic literacy program reflecting this structural property of the script.

Children’s use of linguistic information when learning in a bilingual context

Children prefer to learn from people who are like themselves. However, who is considered “like themselves” is complex for bilingual children. Thus, the current study examined whether children’s language experiences affect who they prefer to imitate. A sample of 3- to 5-year-old monolingual English-speaking children (n=16), Japanese–English bilingual children (n=16), and children bilingual in English and a non-Japanese language (n=16) watched videos of a monolingual English speaker and a Japanese–English bilingual speaker playing with novel toys and were asked to play with the same novel toys. Although all children—regardless of language background—imitated the monolingual speaker at similar rates, the two bilingual groups imitated the bilingual speaker more often than did the monolingual children. Such results suggest that experience in speaking two languages affects children’s imitation behaviors.

Intuitionistic hesitant linguistic sets and their application in multi-criteria decision-making problems

In practice, the use of linguistic information is flexible and definite due to the complexity of problems, and therefore the linguistic models have been widely studied and applied to solve multi-criteria decision-making (MCDM) problems under uncertainty. In this paper, intuitionistic hesitant linguistic sets (IHLSs) are defined on the basis of intuitionistic linguistic sets (ILSs) and hesitant fuzzy linguistic sets (HFLSs). As an evaluation value of one reference object, an intuitionistic hesitant linguistic number (IHLN) contains a linguistic term, a set of membership degrees and a set of non-membership degrees. There can be a consensus on the linguistic term and then decision makers can express their opinions on membership or non-membership degrees depending on their preferences. Therefore, by means of IHLSs, the flexibility in generating evaluation information under uncertainty can be achieved to a larger extent than either ILSs or HFLSs do. Besides, the basic operations and comparison method of IHLNs are studied, which is followed by the definitions of several aggregation operators, including the intuitionistic hesitant linguistic hybrid averaging (IHLHA) operator, the intuitionistic hesitant linguistic hybrid geometric (IHLHG) operator and the corresponding generalized operators. Using these operators, an approach to MCDM problems with intuitionistic hesitant linguistic information is proposed. Finally, an illustrative example is provided to verify the proposed approach and its accuracy and effectiveness have been demonstrated through the comparative analysis with ILSs and HFLSs.

Linguistically-augmented perplexity-based data selection for language models

This paper explores the use of linguistic information for the selection of data to train language models. We depart from the state-of-the-art method in perplexity-based data selection and extend it in order to use word-level linguistic units (i.e. lemmas, named entity categories and part-of-speech tags) instead of surface forms. We then present two methods that combine the different types of linguistic knowledge as well as the surface forms (1, naïve selection of the top ranked sentences selected by each method; 2, linear interpolation of the datasets selected by the different methods). The paper presents detailed results and analysis for four languages with different levels of morphologic complexity (English, Spanish, Czech and Chinese). The interpolation-based combination outperforms the purely statistical baseline in all the scenarios, resulting in language models with lower perplexity. In relative terms the improvements are similar regardless of the language, with perplexity reductions achieved in the range 7.72–13.02%. In absolute terms the reduction is higher for languages with high type-token ratio (Chinese, 202.16) or rich morphology (Czech, 81.53) and lower for the remaining languages, Spanish (55.2) and English (34.43 on the English side of the same parallel dataset as for Czech and 61.90 on the same parallel dataset as for Spanish).

FLINTSTONES: A fuzzy linguistic decision tools enhancement suite based on the 2-tuple linguistic model and extensions

Uncertainty in real world decision making problems not always has probabilistic nature, in such cases the use of linguistic information to model and manage such an uncertainty has given good results. The adoption of linguistic information implies the accomplishment of processes of computing with words to solve linguistic decision making problems. In the specialized literature, several computational models can be found to carry out such processes. However, there is a shortage of software tools that develop and implement these computational models. The 2-tuple linguistic model has been widely used to operate with linguistic information in decision problems due to the fact that provides linguistic results that are accurate and easy to understand for human beings. Furthermore, another advantage of the 2-tuple linguistic model is the existence of different extensions to accomplish processes of computing with words in complex decision frameworks. Due to these reasons, in this paper a fuzzy linguistic decision tools enhancement suite so-called Flintstones is proposed to solve linguistic decision making problems based on the 2-tuple linguistic model and its extensions. Additionally, the Flintstones website is also presented, this website has been deployed and includes a repository of case studies and datasets for different linguistic decision making problems. Finally, a case study solved by Flintstones is illustrated in order to show its performance, usefulness and effectiveness.

A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets

The complexity and impact of many real world decision making problems lead to the necessity of considering multiple points of view, building group decision making problems in which a group of experts provide their preferences to achieve a solution. In such complex problems uncertainty is often present and although the use of linguistic information has provided successful results in managing it, these are sometimes limited because the linguistic models use single-valued and predefined terms that restrict the richness of freely eliciting the preferences of the experts. Usually, experts may doubt between different linguistic terms and require richer expressions to express their knowledge more accurately. However, linguistic group decision making approaches do not provide any model to make more flexible the elicitation of linguistic preferences in such hesitant situations. In this paper is proposed a new linguistic group decision model that facilitates the elicitation of flexible and rich linguistic expressions, in particular through the use of comparative linguistic expressions, close to human beings’ cognitive models for expressing linguistic preferences based on hesitant fuzzy linguistic term sets and context-free grammars. This model defines the group decision process and the necessary operators and tools to manage such linguistic expressions.

Speech Rhythm Facilitates Syntactic Ambiguity Resolution: ERP Evidence

In the current event-related potential (ERP) study, we investigated how speech rhythm impacts speech segmentation and facilitates the resolution of syntactic ambiguities in auditory sentence processing. Participants listened to syntactically ambiguous German subject- and object-first sentences that were spoken with either regular or irregular speech rhythm. Rhythmicity was established by a constant metric pattern of three unstressed syllables between two stressed ones that created rhythmic groups of constant size. Accuracy rates in a comprehension task revealed that participants understood rhythmically regular sentences better than rhythmically irregular ones. Furthermore, the mean amplitude of the P600 component was reduced in response to object-first sentences only when embedded in rhythmically regular but not rhythmically irregular context. This P600 reduction indicates facilitated processing of sentence structure possibly due to a decrease in processing costs for the less-preferred structure (object-first). Our data suggest an early and continuous use of rhythm by the syntactic parser and support language processing models assuming an interactive and incremental use of linguistic information during language processing.

An analysis of symbolic linguistic computing models in decision making

It is common that experts involved in complex real-world decision problems use natural language for expressing their knowledge in uncertain frameworks. The language is inherent vague, hence probabilistic decision models are not very suitable in such cases. Therefore, other tools such as fuzzy logic and fuzzy linguistic approaches have been successfully used to model and manage such vagueness. The use of linguistic information implies to operate with such a type of information, i.e. processes of computing with words (CWW). Different schemes have been proposed to deal with those processes, and diverse symbolic linguistic computing models have been introduced to accomplish the linguistic computations. In this paper, we overview the relationship between decision making and CWW, and focus on symbolic linguistic computing models that have been widely used in linguistic decision making to analyse if all of them can be considered inside of the CWW paradigm.

A consensus support model based on linguistic information for the initial-self assessment of the EFQM in health care organizations

The improvement of the quality of the services is one of the primary sources of competitive advantage in health care organizations. As customers typically search for higher quality of care when choosing treatments, health plans and providers, the health care organizations strive to improve the quality of care and patient safety and satisfaction, as primary goals, with the resources that are available. To do so, the European Foundation for Quality Management (EFQM) Excellence Model for self-assessment has been used by the health care organizations to improve their services and their competitiveness in the global market. However, when the health care organizations address self-assessment processes for the first time, the initial effort needed presents many difficulties. The aim of this paper is to offer a consensus support model based on linguistic information to conduct the self-assessment of the EFQM Excellence Model when questionnaires are used. It is based on the use of linguistic information to provide the individuals’ opinions, which facilitates the individual responses, and on the use of fuzzy majority, represented by means of a linguistic quantifier, to compute the measures guiding the consensus reaching process.

Knowing a lot for one’s age: Vocabulary skill and not age is associated with anticipatory incremental sentence interpretation in children and adults

Adults can incrementally combine information from speech with astonishing speed to anticipate future words. Concurrently, a growing body of work suggests that vocabulary ability is crucially related to lexical processing skills in children. However, little is known about this relationship with predictive sentence processing in children or adults. We explore this question by comparing the degree to which an upcoming sentential theme is anticipated by combining information from a prior agent and action. 48 children, aged of 3 to 10, and 48 college-aged adults’ eye-movements were recorded as they heard a sentence (e.g., The pirate hides the treasure) in which the object referred to one of four images that included an agent-related, action-related and unrelated distractor image. Pictures were rotated so that, across all versions of the study, each picture appeared in all conditions, yielding a completely balanced within-subjects design. Adults and children quickly made use of combinatory information available at the action to generate anticipatory looks to the target object. Speed of anticipatory fixations did not vary with age. When controlling for age, individuals with higher vocabularies were faster to look to the target than those with lower vocabulary scores. Together, these results support and extend current views of incremental processing in which adults and children make use of linguistic information to continuously update their mental representation of ongoing language.

An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges

Many real world problems need to deal with uncertainty, therefore the management of such uncertainty is usually a big challenge. Hence, different proposals to tackle and manage the uncertainty have been developed. Probabilistic models are quite common, but when the uncertainty is not probabilistic in nature other models have arisen such as fuzzy logic and the fuzzy linguistic approach. The use of linguistic information to model and manage uncertainty has given good results and implies the accomplishment of processes of computing with words. A bird’s eye view in the recent specialized literature about linguistic decision making, computing with words, linguistic computing models and their applications shows that the 2-tuple linguistic representation model [44] has been widely-used in the topic during the last decade. This use is because of reasons such as, its accuracy, its usefulness for improving linguistic solving processes in different applications, its interpretability, its ease managing of complex frameworks in which linguistic information is included and so forth. Therefore, after a decade of extensive and intensive successful use of this model in computing with words for different fields, it is the right moment to overview the model, its extensions, specific methodologies, applications and discuss challenges in the topic.

Computing with Words in Decision support Systems: An overview on Models and Applications

Decision making is inherent to mankind, as human beings daily face situations in which they should choose among different alternatives by means of reasoning and mental processes. Many of these decision problems are under uncertain environments with vague and imprecise information. This type of information is usually modelled by linguistic information because of the common use of language by the experts involved in the given decision situations, originating linguistic decision making. The use of linguistic information in decision making demands processes of Computing with Words to solve the related decision problems. Different methodologies and approaches have been proposed to accomplish such processes in an accurate and interpretable way. The good performance of linguistic computing dealing with uncertainty has caused a spread use of it in different types of decision based applications. This paper overviews the more significant and extended linguistic computing models due to its key role in linguistic decision making and a wide range of the most recent applications of linguistic decision support models.

Dealing With Complex Linguistic Annotations Within a Language Processing Framework

In this paper we present AWA, a general purpose Annotation Web Architecture for representing, storing, and accessing the information produced by different linguistic processors. The objective of AWA is to establish a coherent and flexible representation scheme that will be the basis for the exchange and use of linguistic information. In morphologically-rich languages as Basque it is necessary to represent and provide easy access to complex phenomena such as intraword structure, declension, derivation and composition features, constituent discontinousness (in multiword expressions) and so on. AWA provides a well-suited schema to deal with these phenomena. The annotation model relies on XML technologies for data representation, storage and retrieval. Typed feature structures are used as a representation schema for linguistic analyses. A consistent underlying data model, which captures the structure and relations contained in the information to be manipulated, has been identified and implemented. AWA is integrated into LPAF, a multilayered Language Processing and Annotation Framework, whose goal is the management and integration of diverse NLP components and resources. Moreover, we introduce EULIA, an annotation tool which exploits and manipulates the data created by the linguistic processors. Two real corpora have been processed and annotated within this framework.

Improving the voice pitch discrimination threshold through cochlear implants

Aiming at the maximum use of linguistic information conveyed by the periodicity pitch of speech sound through cochlear implants, the possibility of improving the voice pitch discrimination threshold was examined for cochlear implantees: three acquired deaf adults and four congenitally deaf children. Female and male adult speech samples of vowel /a/ were analyzed and resynthesized using the STRAIGHT computer program to shift the original voice pitch (c1 and c) one octave upward and downward with a half‐step interval on the musical scale. By presenting those synthetic speech samples in pairs, the discrimination threshold of the voice pitch interval was observed. Association of the speech samples to the picture of a piano keyboard was helpful for conceptualizing the voice pitch change, especially for children. Preliminary observation results suggested that the discrimination threshold can be improved by less than one whole step by adults and two whole steps by children. Those figures were promising because, even for Japanese word accent and Chinese tones, which were conveyed by the precise changes in voice pitch, it was ascertained by acoustical analyses of both their production and perception that discrimination of a half‐step difference was small enough to recognize their pitch patterns.

Forming a stable spatial representation

The visual system sometimes fails, partially or completely, to encode and/or retrieve spatial relations among parts of an object. For example, targets can easily be confused with their mirror images, especially when they must be retained in memory. In the current experiments we ask whether our representations of spatial relations can be amended by information from different cognitive domains. Specifically, we ask whether failure to form a stable representation of spatial relations among parts can be overcome by the use of linguistic information. Four year-olds saw squares split by color and matched them after delay. In Experiment 1, children saw the target and were told either “Look, this is a blicket” (Label Condition) or “Look!” (NoLabel Condition). Then, three choices appeared: the target (e.g. vertical split with red left, green right), its mirror image, and another square that had a different internal split (e.g. horizontal). Overall, children performed better than chance. However, their errors were almost exclusively mirror image confusions, suggesting that children failed to bind color and location (e.g. red left, green right). There was no difference between the NoLabel and Label conditions, suggesting the whole-object novel label did not help children form a stable representation of the spatial relation among the parts. Experiment 2 tested whether color–location binding can be improved by providing language that might bind these features. Children were shown a target and were told, e.g. “The red is on the left.” Performance was reliably better than in Experiment 1, suggesting language did help children bind color and location. Experiments 3 and 4 explored whether the same performance improvement could be accomplished by increasing non-linguistic attention to the target (i.e. flashing the red part, Experiment 3) or by using neutral relational language (e.g. “The red is touching the green”). Neither experiment showed enhanced performance, suggesting that language can augment visual–spatial representations only if it conveys very specific information (e.g. direction). Generally, the results suggest that specific linguistic information can help form a stable representation of spatial relationship and that this effect is not attributable to general attentional effects.

EXTENSIVE EVALUATION OF EFFICIENT NLP-DRIVEN TEXT CLASSIFICATION

Extensive experimental evidence is required to study the impact of text categorization approaches on real data and to assess the performance within operational scenarios. In this paper a wide set of profile-based classification models (a class of very efficient classifiers) sensitive to the syntactic information extracted from source texts is discussed. Several classifiers are tested, ranging from traditional approaches (e.g., variants of vector space, like SMART, or linear regression models) to original methods. All the experiments aim to evaluate some newly introduced feature weighting and inference models as well as to characterize the role of different linguistic information. The final purpose is thus to give an insight on the effective and efficient use of linguistic information for text categorization. The results suggest that an optimal exploitation of linguistic features can be obtained by a suitable selection among methods of feature weighting and inference. The empirical evidence collected in this paper over a wide range of corpora and languages is retained as a useful basis for the systematic design of operational statistical NLP-driven text classifiers.

Evaluating the use of linguistic information in the pre-processing phase of Text Mining

This work proposes and evaluates the use of linguistic information in the pre-processing phase for text mining tasks applied to Portuguese texts. We present several experiments comparing our proposal to the usual techniques applied in the field. The results show that the use of linguistic information in the pre-processing phase brings some improvements for both text categorization and clustering.

INTELLIGENT NLP-DRIVEN TEXT CLASSIFICATION

Information Retrieval (IR) and NLP-driven Information Extraction (IE) are complementary activities. IR helps in locating specific documents within a huge search space (localization) while IE supports the localization of specific information within a document (extraction or explanation). In application scenarios both capabilities are usually needed. IE is important here, as it can enrich the IR inferences with motivating information. Works on Web-based IR suggest that embedding linguistic information (e.g. sense distinctions) at a suitable level within traditional quantitative approaches (e.g. query expansion as in [26]) is a promising approach. "Which linguistic level is best suited to which IR mechanism" is the interesting representational problem posed by the current research stage. This is also the central concern of this paper. A traditional method for efficient text categorization is here presented. Original features of the proposed model are a self-adapting parameterized weighting model and the use of linguistic information. The key idea is the integration of NLP methods within a robust and efficient TC framework. This allows to combine benefits of large scale and efficient IR with the richer expressivity closer to IE. In this paper we capitalize the systematic benchmarking resources available in TC to extensively derive empirical evidence about the above representational problem. The positive experimental results confirm that the proposed TC framework characterizes as a viable approach to intelligent text categorization on a large scale.

THE 2-TUPLE LINGUISTIC COMPUTATIONAL MODEL: ADVANTAGES OF ITS LINGUISTIC DESCRIPTION, ACCURACY AND CONSISTENCY

The Fuzzy Linguistic Approach has been applied successfully to different areas. The use of linguistic information for modelling expert preferences implies the use of processes of Computing with Words. To accomplish these processes different approaches has been proposed in the literature: (i) Computational model based on the Extension Principle, (ii) the symbolic one(also called ordinal approach), and (iii) the 2-tuple linguistic computational model. The main problem of the classical approaches, (i) and (ii), is the loss of information and lack of precision during the computational processes. In this paper, we want to compare the linguistic description, accuracy and consistency of the results obtained using each model over the rest ones. To do so, we shall solve a Multiexpert Multicriteria Decision-Making problem defined in a multigranularity linguistic context using the different computational approaches. This comparison helps us to decide what model is more adequated for computing with words.