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Numeral Classifiers in Udi: A Unique Contact-Induced Development among Nakh-Daghestanian?
Abstract Following Stilo’s (2018) study of small-inventory classifier systems in a number of Indo-European, Turkic, Kartvelian and Semitic languages of the Araxes-Iran Linguistic Area, the paper presents an account of numeral classifiers in Udi, a Nakh-Daghestanian (Lezgic) language spoken in northern Azerbaijan. Being a peripheral member of the linguistic area in question, Udi possesses an even more reduced version of a small-classifier system, comprising one optional classifier dänä (Iranian borrowing, most likely via Azerbaijani) used with both human and inanimate nouns. A dedicated classifier for humans is lacking, although there is a word tan (also of Iranian origin) only used after numerals or quantifiers, but predominantly as a noun phrase head. The behaviour of dänä and tan is scrutinized, according to a set of parameters, in both spoken and written textual corpora of the Nizh dialect of Udi. Drawing in the data from the related Nakh-Daghestanian languages, the paper shows that among the languages of the family Udi may be unique in possessing classifiers (albeit as a result of contact), Khinalug possibly being the only other exception.
In the present paper, we present a detailed description of the classifier systems of five Indian languages-- Mizo, Galo, Tagin (all belongs to the Tibeto-Burman family), Assamese (Indo-Aryan) and Malto (Dravidian). It is observed that the classifiers are a predominant feature in the Tibeto-Burman and we observe an extensive classifier system in these languages. There is no equivalent classifier system in other language families. However in the languages belonging to Eastern India, irrespective of the family, there is some sort of classifier system. Thus classifiers seem to be an areal feature in most of the Eastern and whole of the North-Eastern India. The purpose of the paper is to study if there is some semantic similarity among the classifier systems across language families in this area and thus to see if it is indeed an areal feature. It is just a preliminary description of an ongoing research in which we intend to study many more languages and include languages from the Austro-Asiatic family (such as Khasi and Munda languages spoken in Jharkhand) as well.
Patients with chronic diseases are frequent users of healthcare services. The systematic use of stratification tools and predictive models for this group of patients can be useful for health professionals in decision-making processes. The aim of this study was to design two new classifier systems for detecting the risk of hospital admission for elderly patients with chronic conditions. In this retrospective cohort study, a set of variables related to hospital admission for patients with chronic conditions was obtained through focus groups, a health database analysis and statistical processing. To predict the probability of admission from the set of predictor variables, a logistic regression within the framework of Generalized Linear Models was used. The target population consisted of patients aged 65 years or older treated in February 2016 at the Primary Health Care Centre of Burjassot (Spain). This sample was selected through the consecutive sampling of the patient quotas of the physicians who participated in the study (1000 patients). The result was two classification systems, with reasonable values of 0.722 and 0.744 for the area under the ROC curve. The proposed classifier systems could facilitate a change in the current patient management models and make them more proactive.
<p>Learning is an important activity through which humanity has incrementally improved accomplishing tasks by adapting knowledge and methods based on the related feedback. Although learning is natural to humans, it is much more difficult to achieve in the technological world as tasks are often learned in isolation. Software is capable of learning novel techniques and algorithms in order to solve these basic, individual problems, however transferring said knowledge to other problems in the same or related domains presents challenges. Solutions often cannot be enumerated to discover the best one as many problems of interest can be intractable in terms of the resources needed to successfully complete them. However, many such problems contain key building blocks of knowledge that can be leveraged to achieve a suitable solution. These building blocks encapsulate important structural regularities of the problem. A technique that can learn these regularities without enumeration,may produce general solutions that apply to similar problems of any length. This implies reusing learned information. In order to reuse learned blocks of knowledge, it is important that a program be scalable and flexible. This requires a program capable of taking knowledge from a previous task and applying it to a more complex problem or a problem with a similar pattern. This is anticipated to enable the program to complete the new task in a practical amount of time and with reasonable amounts of resources. In machine learning, the degree of human intervention in solving problems is often important in many tasks. It is generally necessary for a human to provide input to direct and improve learning. In the field of Developmental Learning there is the idea known as the Threshold Concept (TC). A TC is transformative information which advocates learning. TCs are important because without them, the learner cannot progress. In addition, TCs need to be learned in a particular order, much like a curriculum, thus providing the student with viable progress towards learning more difficult ideas at a faster pace than otherwise. Therefore, human input to a learning algorithm can be to partition a problem into constituent subproblems. This is a principal concept of Layered Learning (LL),where a sequence of sub-problems are learned. The sub-problems are self-contained stages which have been separated by a human. This technique is necessary for tasks in which learning a direct mapping from inputs to outputs is intractable given existing learning algorithms. One of the first artificial learning systems developed is Learning Classifier Systems (LCSs). Past work has extended LCSs to provide more expressivity by using richer representations. One such representation is tree-based and is common to the Genetic Programming (GP) technique. GP is part of the Evolutionary Computation (EC) paradigm and produces solutions represented by trees. The tree nodes can contain functions, and leaf nodes problem features, giving GP a rich representation. A more recent technique is Code Fragments (CFs). CFs are GP-like sub-trees with an initial maximum height of two. Initially, CFs contained hard-coded functions at the root nodes and problem features or previously learned CFs at the leaf nodes of the sub-trees. CFs provided improved expressivity and scalability over the original ternary alphabet used by LCSs. Additionally, CF-based systems have successfully learned previously intractable problems, e.g. 135-bit multiplexer. Although CFs have provided increased scalability, they suffer from a structural weakness. As the problem scales, the chains of CFs grow to intractable lengths. This means that at some point the LCS will stop learning. In addition, CFs were originally meant to scale to more complex problems in the same domain. However, it is advantageous to compile cross-domain solutions, as the regularities of a problem might be from different domains to that expressed by the data. The proposed thesis is that a CF-based LCS can scale to complex problems by reusing learned solutions of problems as functions at the inner nodes of CFs together with compaction and Layered Learning. The overall goal is divided into the following three sub-goals: reuse learned functionality from smaller problems in the root nodes of CF sub-trees, identify a compaction technique that facilitates reduced solution size for improved evaluation time of CFs and develop a layered learning methodology for a CF system, which will be demonstrated by learning a general solution to an intractable problem, i.e. n-bit Multiplexer. In this novel work, Code Fragments are extended to include learned functionality at the root nodes of the sub-trees in a technique known as XCSCF². A new compaction technique is designed, which produces an equivalent set of ternary rules from CF rules. This technique is known as XCSCF3. The work culminates with a new technique XCSCF*, which combines Layered Learning, Code Fragments and Transfer Learning (TL) of knowledge and functionality to produce scalable and general solutions, i.e. to the n-bit multiplexer problem. The novel ideas are tested with the multiplexer and hidden multiplexer problems. These problems are chosen because they are difficult due to epistasis, sparsity and non-linearity. Therefore they provide ample opportunity for testing the new contributions. The thesis work has shown that CFs can be used in various ways to increase scalability and to discover solutions to complex problems. Specifically the following three contributions were produced: learned functionality was captured in LCS populations from smaller problems and was reused in the root nodes of CF sub-trees. An online compaction technique that facilitates reduced evaluation time of CFs was designed. A layered learning method to train a CF system in a manner leading to a general solution was developed. This was demonstrated through learning a solution to a previously intractable problem, i.e. the n-bit Multiplexer. The thesis concludes with suggestions for future work aimed at providing better scalability when using compaction techniques.</p>
<p>Learning is an important activity through which humanity has incrementally improved accomplishing tasks by adapting knowledge and methods based on the related feedback. Although learning is natural to humans, it is much more difficult to achieve in the technological world as tasks are often learned in isolation. Software is capable of learning novel techniques and algorithms in order to solve these basic, individual problems, however transferring said knowledge to other problems in the same or related domains presents challenges. Solutions often cannot be enumerated to discover the best one as many problems of interest can be intractable in terms of the resources needed to successfully complete them. However, many such problems contain key building blocks of knowledge that can be leveraged to achieve a suitable solution. These building blocks encapsulate important structural regularities of the problem. A technique that can learn these regularities without enumeration,may produce general solutions that apply to similar problems of any length. This implies reusing learned information. In order to reuse learned blocks of knowledge, it is important that a program be scalable and flexible. This requires a program capable of taking knowledge from a previous task and applying it to a more complex problem or a problem with a similar pattern. This is anticipated to enable the program to complete the new task in a practical amount of time and with reasonable amounts of resources. In machine learning, the degree of human intervention in solving problems is often important in many tasks. It is generally necessary for a human to provide input to direct and improve learning. In the field of Developmental Learning there is the idea known as the Threshold Concept (TC). A TC is transformative information which advocates learning. TCs are important because without them, the learner cannot progress. In addition, TCs need to be learned in a particular order, much like a curriculum, thus providing the student with viable progress towards learning more difficult ideas at a faster pace than otherwise. Therefore, human input to a learning algorithm can be to partition a problem into constituent subproblems. This is a principal concept of Layered Learning (LL),where a sequence of sub-problems are learned. The sub-problems are self-contained stages which have been separated by a human. This technique is necessary for tasks in which learning a direct mapping from inputs to outputs is intractable given existing learning algorithms. One of the first artificial learning systems developed is Learning Classifier Systems (LCSs). Past work has extended LCSs to provide more expressivity by using richer representations. One such representation is tree-based and is common to the Genetic Programming (GP) technique. GP is part of the Evolutionary Computation (EC) paradigm and produces solutions represented by trees. The tree nodes can contain functions, and leaf nodes problem features, giving GP a rich representation. A more recent technique is Code Fragments (CFs). CFs are GP-like sub-trees with an initial maximum height of two. Initially, CFs contained hard-coded functions at the root nodes and problem features or previously learned CFs at the leaf nodes of the sub-trees. CFs provided improved expressivity and scalability over the original ternary alphabet used by LCSs. Additionally, CF-based systems have successfully learned previously intractable problems, e.g. 135-bit multiplexer. Although CFs have provided increased scalability, they suffer from a structural weakness. As the problem scales, the chains of CFs grow to intractable lengths. This means that at some point the LCS will stop learning. In addition, CFs were originally meant to scale to more complex problems in the same domain. However, it is advantageous to compile cross-domain solutions, as the regularities of a problem might be from different domains to that expressed by the data. The proposed thesis is that a CF-based LCS can scale to complex problems by reusing learned solutions of problems as functions at the inner nodes of CFs together with compaction and Layered Learning. The overall goal is divided into the following three sub-goals: reuse learned functionality from smaller problems in the root nodes of CF sub-trees, identify a compaction technique that facilitates reduced solution size for improved evaluation time of CFs and develop a layered learning methodology for a CF system, which will be demonstrated by learning a general solution to an intractable problem, i.e. n-bit Multiplexer. In this novel work, Code Fragments are extended to include learned functionality at the root nodes of the sub-trees in a technique known as XCSCF². A new compaction technique is designed, which produces an equivalent set of ternary rules from CF rules. This technique is known as XCSCF3. The work culminates with a new technique XCSCF*, which combines Layered Learning, Code Fragments and Transfer Learning (TL) of knowledge and functionality to produce scalable and general solutions, i.e. to the n-bit multiplexer problem. The novel ideas are tested with the multiplexer and hidden multiplexer problems. These problems are chosen because they are difficult due to epistasis, sparsity and non-linearity. Therefore they provide ample opportunity for testing the new contributions. The thesis work has shown that CFs can be used in various ways to increase scalability and to discover solutions to complex problems. Specifically the following three contributions were produced: learned functionality was captured in LCS populations from smaller problems and was reused in the root nodes of CF sub-trees. An online compaction technique that facilitates reduced evaluation time of CFs was designed. A layered learning method to train a CF system in a manner leading to a general solution was developed. This was demonstrated through learning a solution to a previously intractable problem, i.e. the n-bit Multiplexer. The thesis concludes with suggestions for future work aimed at providing better scalability when using compaction techniques.</p>
<p>Using evolutionary intelligence and machine learning techniques, a broad range of intelligent machines have been designed to perform different tasks. An intelligent machine learns by perceiving its environmental status and taking an action that maximizes its chances of success. Human beings have the ability to apply knowledge learned from a smaller problem to more complex, large-scale problems of the same or a related domain, but currently the vast majority of evolutionary machine learning techniques lack this ability. This lack of ability to apply the already learned knowledge of a domain results in consuming more than the necessary resources and time to solve complex, large-scale problems of the domain. As the problem increases in size, it becomes difficult and even sometimes impractical (if not impossible) to solve due to the needed resources and time. Therefore, in order to scale in a problem domain, a systemis needed that has the ability to reuse the learned knowledge of the domain and/or encapsulate the underlying patterns in the domain. To extract and reuse building blocks of knowledge or to encapsulate the underlying patterns in a problem domain, a rich encoding is needed, but the search space could then expand undesirably and cause bloat, e.g. as in some forms of genetic programming (GP). Learning classifier systems (LCSs) are a well-structured evolutionary computation based learning technique that have pressures to implicitly avoid bloat, such as fitness sharing through niche based reproduction. The proposed thesis is that an LCS can scale to complex problems in a domain by reusing the learnt knowledge from simpler problems of the domain and/or encapsulating the underlying patterns in the domain. Wilson’s XCS is used to implement and test the proposed systems, which is a well-tested, online learning and accuracy based LCS model. To extract the reusable building blocks of knowledge, GP-tree like, code-fragments are introduced, which are more than simply another representation (e.g. ternary or real-valued alphabets). This thesis is extended to capture the underlying patterns in a problemusing a cyclic representation. Hard problems are experimented to test the newly developed scalable systems and compare them with benchmark techniques. Specifically, this work develops four systems to improve the scalability of XCS-based classifier systems. (1) Building blocks of knowledge are extracted fromsmaller problems of a Boolean domain and reused in learning more complex, large-scale problems in the domain, for the first time. By utilizing the learnt knowledge from small-scale problems, the developed XCSCFC (i.e. XCS with Code-Fragment Conditions) system readily solves problems of a scale that existing LCS and GP approaches cannot, e.g. the 135-bitMUX problem. (2) The introduction of the code fragments in classifier actions in XCSCFA (i.e. XCS with Code-Fragment Actions) enables the rich representation of GP, which when couples with the divide and conquer approach of LCS, to successfully solve various complex, overlapping and niche imbalance Boolean problems that are difficult to solve using numeric action based XCS. (3) The underlying patterns in a problem domain are encapsulated in classifier rules encoded by a cyclic representation. The developed XCSSMA system produces general solutions of any scale n for a number of important Boolean problems, for the first time in the field of LCS, e.g. parity problems. (4) Optimal solutions for various real-valued problems are evolved by extending the existing real-valued XCSR system with code-fragment actions to XCSRCFA. Exploiting the combined power of GP and LCS techniques, XCSRCFA successfully learns various continuous action and function approximation problems that are difficult to learn using the base techniques. This research work has shown that LCSs can scale to complex, largescale problems through reusing learnt knowledge. The messy nature, disassociation of message to condition order, masking, feature construction, and reuse of extracted knowledge add additional abilities to the XCS family of LCSs. The ability to use rich encoding in antecedent GP-like codefragments or consequent cyclic representation leads to the evolution of accurate, maximally general and compact solutions in learning various complex Boolean as well as real-valued problems. Effectively exploiting the combined power of GP and LCS techniques, various continuous action and function approximation problems are solved in a simple and straight forward manner. The analysis of the evolved rules reveals, for the first time in XCS, that no matter how specific or general the initial classifiers are, all the optimal classifiers are converged through the mechanism ‘be specific then generalize’ near the final stages of evolution. Also that standard XCS does not use all available information or all available genetic operators to evolve optimal rules, whereas the developed code-fragment action based systems effectively use figure and ground information during the training process. Thiswork has created a platformto explore the reuse of learnt functionality, not just terminal knowledge as present, which is needed to replicate human capabilities.</p>
<p>Using evolutionary intelligence and machine learning techniques, a broad range of intelligent machines have been designed to perform different tasks. An intelligent machine learns by perceiving its environmental status and taking an action that maximizes its chances of success. Human beings have the ability to apply knowledge learned from a smaller problem to more complex, large-scale problems of the same or a related domain, but currently the vast majority of evolutionary machine learning techniques lack this ability. This lack of ability to apply the already learned knowledge of a domain results in consuming more than the necessary resources and time to solve complex, large-scale problems of the domain. As the problem increases in size, it becomes difficult and even sometimes impractical (if not impossible) to solve due to the needed resources and time. Therefore, in order to scale in a problem domain, a systemis needed that has the ability to reuse the learned knowledge of the domain and/or encapsulate the underlying patterns in the domain. To extract and reuse building blocks of knowledge or to encapsulate the underlying patterns in a problem domain, a rich encoding is needed, but the search space could then expand undesirably and cause bloat, e.g. as in some forms of genetic programming (GP). Learning classifier systems (LCSs) are a well-structured evolutionary computation based learning technique that have pressures to implicitly avoid bloat, such as fitness sharing through niche based reproduction. The proposed thesis is that an LCS can scale to complex problems in a domain by reusing the learnt knowledge from simpler problems of the domain and/or encapsulating the underlying patterns in the domain. Wilson’s XCS is used to implement and test the proposed systems, which is a well-tested, online learning and accuracy based LCS model. To extract the reusable building blocks of knowledge, GP-tree like, code-fragments are introduced, which are more than simply another representation (e.g. ternary or real-valued alphabets). This thesis is extended to capture the underlying patterns in a problemusing a cyclic representation. Hard problems are experimented to test the newly developed scalable systems and compare them with benchmark techniques. Specifically, this work develops four systems to improve the scalability of XCS-based classifier systems. (1) Building blocks of knowledge are extracted fromsmaller problems of a Boolean domain and reused in learning more complex, large-scale problems in the domain, for the first time. By utilizing the learnt knowledge from small-scale problems, the developed XCSCFC (i.e. XCS with Code-Fragment Conditions) system readily solves problems of a scale that existing LCS and GP approaches cannot, e.g. the 135-bitMUX problem. (2) The introduction of the code fragments in classifier actions in XCSCFA (i.e. XCS with Code-Fragment Actions) enables the rich representation of GP, which when couples with the divide and conquer approach of LCS, to successfully solve various complex, overlapping and niche imbalance Boolean problems that are difficult to solve using numeric action based XCS. (3) The underlying patterns in a problem domain are encapsulated in classifier rules encoded by a cyclic representation. The developed XCSSMA system produces general solutions of any scale n for a number of important Boolean problems, for the first time in the field of LCS, e.g. parity problems. (4) Optimal solutions for various real-valued problems are evolved by extending the existing real-valued XCSR system with code-fragment actions to XCSRCFA. Exploiting the combined power of GP and LCS techniques, XCSRCFA successfully learns various continuous action and function approximation problems that are difficult to learn using the base techniques. This research work has shown that LCSs can scale to complex, largescale problems through reusing learnt knowledge. The messy nature, disassociation of message to condition order, masking, feature construction, and reuse of extracted knowledge add additional abilities to the XCS family of LCSs. The ability to use rich encoding in antecedent GP-like codefragments or consequent cyclic representation leads to the evolution of accurate, maximally general and compact solutions in learning various complex Boolean as well as real-valued problems. Effectively exploiting the combined power of GP and LCS techniques, various continuous action and function approximation problems are solved in a simple and straight forward manner. The analysis of the evolved rules reveals, for the first time in XCS, that no matter how specific or general the initial classifiers are, all the optimal classifiers are converged through the mechanism ‘be specific then generalize’ near the final stages of evolution. Also that standard XCS does not use all available information or all available genetic operators to evolve optimal rules, whereas the developed code-fragment action based systems effectively use figure and ground information during the training process. Thiswork has created a platformto explore the reuse of learnt functionality, not just terminal knowledge as present, which is needed to replicate human capabilities.</p>
Abstract Our study compares Standard Mandarin (the Beijing dialect used in spoken and written registers) with the Mandarin dialect of Baoding (one of the Mandarin dialects belonging to the Jì-lŭ Mandarin group, Hebei-Shandong). Standard Mandarin and Baoding are geographically and phylogenetically closely related, but they differ in terms of their classifier system, as Standard Mandarin resorts to a wide array of sortal classifiers whereas Baoding only uses one general classifier. We first provide a detailed analysis of the unconventional classifier system in Baoding. Then, we compare the lexical and discourse functions of sortal classifiers in Standard Mandarin and Baoding. We show that Standard Mandarin does present a certain level of convergence with its geographical neighbour Baoding. However, these varieties also display significant divergences, as several lexical and discourse functions typically associated with classifier systems cannot be fulfilled by the only classifier found in Boading.
Abstract Noun categorization devices, or classifiers, of all types are a means of classifying referents in terms of basic cognitively salient parameters. These include humanness, animacy, sex, shape, direction and orientation, consistency, and function. In large systems of classifiers, one finds additional terms whose application is restricted to a limited set of referents, or even just to a single referent. For instance, numerous languages of Mainland Southeast Asia have elaborate sets of specific classifiers in the domain of social hierarchies and human interactions. Languages with multiple classifier systems spoken in riverine environment will be likely to have a special classifier for ‘canoe’. Rather than categorizing entities in terms of general features, such classifiers with specific meanings serve to highlight items important for the socio-cultural environment of the speakers and their means of subsistence. Specific classifiers are likely to be lost if a practice or a hierarchy they reflect undergoes attrition. They occupy a singular place in language acquisition and the history of development of classifier systems.
To assess critically the scientific literature is a very challenging task; in general it requires analysing a lot of documents to define the state-of-the-art of a research field and classifying them. The documents classifier systems have tried to address this problem by different techniques such as probabilistic, machine learning and neural networks models. One of the most popular document classification approaches is the LDA (Latent Dirichlet Allocation), a probabilistic topic model. One of the main issues of the LDA approach is that the retrieved topics are a collection of terms with their probabilities and it does not have a human-readable form. This paper defines an approach to make LDA topics comprehensible for humans by the exploitation of the Word2Vec approach.
