Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

Child psychiatrists, pediatricians, and other child clinicians need to have a solid understanding of child language development. There are at least four important reasons that make this necessary. First, slowing, arrest, and deviation of language development are highly associated with, and complicate the course of, child psychopathology. Second, language competence plays a crucial role in emotional and mood regulation, evaluation, and therapy. Third, language deficits are the most frequent underpinning of the learning disorders, ubiquitous in our clinical populations. Fourth, clinicians should not confuse the rich linguistic and dialectal diversity of our clinical populations with abnormalities in child language development. The challenge for the clinician becomes, then, how to get immersed in the captivating field of child language acquisition without getting overwhelmed by its conceptual and empirical complexity. In the past 50 years and since the seminal works of Roger Brown, Jerome Bruner, and Catherine Snow, child language researchers (often known as developmental psycholinguists) have produced a remarkable body of knowledge. Linguists such as Chomsky and philosophers such as Grice have strongly influenced the science of child language. One of the major tenets of Chomskian linguistics (known as generative grammar) is that children’s capacity to acquire language is “hardwired” with “universal grammar”—an innate language acquisition device (LAD), a language “instinct”—at its core. This view is in part supported by the assertion that the linguistic input that children receive is relatively dismal and of poor quality relative to the high quantity and quality of output that they manage to produce after age 2 and that only an advanced, innate capacity to decode and organize linguistic input can enable them to “get from here (prelinguistic infant) to there (linguistic child).” In “Constructing a Language,” Tomasello presents a contrasting theory of how the child acquires language: It is not a universal grammar that allows for language development. Rather, human cognition universals of communicative needs and vocal-auditory processing result in some language universals, such as nouns and verbs as expressions of reference and predication (p. 19). The author proposes that two sets of cognitive skills resulting from biological/phylogenetic adaptations are fundamental to the ontogenetic origins of language. These sets of inherited cognitive skills are intentionreading on the one hand and pattern-finding, on the other. Intention-reading skills encompass the prelinguistic infant’s capacities to share attention to outside events with other persons, establishing joint attentional frames, to understand other people’s communicative intentions, and to imitate the adult’s communicative intentions (an intersubjective form of imitation that requires symbolic understanding and perspective-taking). Pattern-finding skills include the ability of infants as young as 7 months old to analyze concepts and percepts (most relevant here, auditory or speech percepts) and create concrete or abstract categories that contain analogous items. Tomasello, a most prominent developmental scientist with research foci on child language acquisition and on social cognition and social learning in children and primates, succinctly and clearly introduces the major points of his theory and his views on the origins of language in the initial chapters. In subsequent chapters, he delves into the details by covering most language acquisition domains, namely, word (lexical) learning, syntax, and morphology and conversation, narrative, and extended discourse. Although one of the remaining domains (pragmatics) is at the core of his theory and permeates the text throughout, the relative paucity of passages explicitly devoted to discussing acquisition and proBOOK REVIEWS

Constructing a language: A usage-based theory of language acquisition

A grammar of spoken Chinese = 中國話的文法

中國科技大學通識教育中心英語文證照奬勵金實施要點 中華民國 105 年 1 月 8 日通識教育委員會議通過 一、 中國科技大學(以下簡稱本校)為鼓勵本校學生通過具公信力機構之英語文能力測驗或 取得證照,特訂定「中國科技大學通識教育中心英語文證照獎勵金實施要點」(以下簡 稱本要點)。 二、 學生於就讀本校期間,通過歐盟共同架構(CEFR)語言能力參考指標 B1(中級)同等級英 語文能力測驗以上(含)者,得依據本要點酌予獎勵。檢測項目請參閱本中心「歐洲語言 學習、教學、評量共同參考架構與各英語檢測分級對照表」(參見附表);未列於標準 對照表之測驗項目不給予獎助。 三、 凡本校學生,除應英系外,均得申請。大學部學生通過同等級以申請一次為限,在學期 間得重複申請,但該次申請之級別不得低於前次。 本獎勵金每學期核發乙次,每次核發全校前 10 名,各名次核發金額如附表。 四、 申請人應提供在學期間,申請當(學)期參加考試之證明文件及成績證明或證照,以憑辦 理。 五、 獎勵金申請作業:請至通識教育中心網頁下載「英語文證照獎勵金申請表」(附件 1), 填妥後檢附成績單正本及影本(背面簽名並註明與正本無異)各一份、本人金融帳戶存 簿(郵局或土地銀行)封面影本送至通識教育中心。 通識教育中心得每學期遴選受獎代表,擇期公開頒奬,並辦理後續請款作業。 六、 奬勵金申請期限:通過相關證照考試半年內應提出申請,逾期視同放棄。 七、 本要點之獎勵金由學校開設通識教育中心專戶,一切收支專款專用;每年度如有剩餘 款,則移至翌年度繼續使用。 八、 本要點經通識教育中心會議審查通過,陳請校長核定後公告實施,修訂時亦同。

文化と言語の多様性の中のCommon European Framework of Reference for Languages: Learning, teaching, assessment (CEFR)--それは基準か? (第10回明海大学大学院応用言語学研究科セミナー 講演)

1. In trod u otion In this paper, I w ill discuss one aspect of the lexicon, namely its morphological organization. For about two years I have been working with Koakenniemi's twolevel model (Koakenniemi 1983)i on a Polish two-level description. In this work, I have become more and more Interested in the formalism it s e l f , something that has tended to push work on the language description into the background. It seems to be the case that in moat concrete two-level descriptions, the rule component is forced to carry too heavy a burden in comparison with the lexicon, perhaps because the lexicon is very simple as to its implementation. Like many other lexical systems in computer applications i t is implemented as a tree, with a root node and leaves, from which the lexical entries eire retrieved when the analysts routine has traversed the tree. The twolevel lexicon la a bit more sophisticated than th is , however, in that there la not only one, but several lexicon trees, the so called minilexicons. The user links the mlnilexicona into a whole, moat often into a root lexicon and a number of su ffix lexicons. In Hockett'a terminology, we could apeak of an Itern-and-Arrangement (lA) model (Hockett 1958, pp 386ff). Elsewhere I have characterized this kind of lexicon system as most suited for describing suffixing languages with a comparatively high degree of agglutination (Borin 1985f p 35); This is mainly due to the fact that the system works according to what Blåberg (1984, p 6 l) aptly has termed the "forget-where-you-came-from"

/pdf/what-is-a-lexical-representation-jwhi45c3dn.pdf

What is a lexical representation

We present Korp, the corpus infrastructure of Sprakbanken (the Swedish Language Bank). The infrastructure consists of three main components: the Korp corpus pipeline, the Korp backend, and the Korp frontend. The Korp corpus pipeline is used for importing corpora, annotating them, and then exporting the annotated corpora into different formats. An essential feature of the pipeline is the ability to leave existing annotations untouched, both structural and word level annotations, and to use the existing annotations as the foundation of other annotations. The Korp backend consists of a set of REST-based web services for searching in and retrieving information about the corpora. Finally, the Korp frontend is a graphical search interface that interacts with the Korp backend. The interface has been inspired by corpus search interfaces such as SketchEngine, Glossa, and DeepDict, and it uses State Chart XML (SCXML) in order to enable users to bookmark interaction states. We give a functional and technical overview of the three components, followed by a discussion of planned future work.

/pdf/korp-the-corpus-infrastructure-of-sprakbanken-599u139wdf.pdf

Korp — the corpus infrastructure of Språkbanken

This article surveys work on Unsupervised Learning of Morphology. We define Unsupervised Learning of Morphology as the problem of inducing a description (of some kind, even if only morpheme-segmentation) of how orthographic words are built up given only raw text data of a language. We briefly go through the history and motivation of the this problem. Next, over 200 items of work are listed with a brief characterization, and the most important ideas in the field are critically discussed. We summarize the achievements so far and give pointers for future developments.

/pdf/unsupervised-learning-of-morphology-4ibjeuf7oe.pdf

Unsupervised learning of morphology

The English-language Princeton WordNet (PWN) and some wordnets for other languages have been extensively used as lexical---semantic knowledge sources in language technology applications, due to their free availability and their size. The ubiquitousness of PWN-type wordnets tends to overshadow the fact that they represent one out of many possible choices for structuring a lexical---semantic resource, and it could be enlightening to look at a differently structured resource both from the point of view of theoretical---methodological considerations and from the point of view of practical text processing requirements. The resource described here--SALDO--is such a lexical---semantic resource, intended primarily for use in language technology applications, and offering an alternative organization to PWN-style wordnets. We present our work on SALDO, compare it with PWN, and discuss some implications of the differences. We also describe an integrated infrastructure for computational lexical resources where SALDO forms the central component.

SALDO: a touch of yin to WordNet's yang

Our languages are in constant flux driven by external factors such as cultural, societal and technological changes, as well as by only partially understood internal motivations. Words acquire new meanings and lose old senses, new words are coined or borrowed from other languages and obsolete words slide into obscurity. Understanding the characteristics of shifts in the meaning and in the use of words is useful for those who work with the content of historical texts, the interested general public, but also in and of itself. The findings from automatic lexical semantic change detection, and the models of diachronic conceptual change are currently being incorporated in approaches for measuring document across-time similarity, information retrieval from long-term document archives, the design of OCR algorithms, and so on. In recent years we have seen a surge in interest in the academic community in computational methods and tools supporting inquiry into diachronic conceptual change and lexical replacement. This article is an extract of a survey of recent computational techniques to tackle lexical semantic change currently under review. In this article we focus on diachronic conceptual change as an extension of semantic change.

Lars Borin

Papers

What is a lexical representation

Korp — the corpus infrastructure of Språkbanken

Unsupervised learning of morphology

SALDO: a touch of yin to WordNet's yang

Survey of Computational Approaches to Lexical Semantic Change