This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explicitly calculated sample complexity learning times under different input distribution assumptions (including CHILDES database language input) and learning regimes. We also briefly describe a new way to formally model (rapid) diachronic syntax change.
Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started.
Distributional similarity is a classic technique for entity set expansion, where the system is given a set of seed entities of a particular class, and is asked to expand the set using a corpus to obtain more entities of the same class as represented by the seeds. This paper shows that a machine learning model called positive and unlabeled learning (PU learning) can model the set expansion problem better. Based on the test results of 10 corpora, we show that a PU learning technique outperformed distributional similarity significantly. ...
In this paper, we present a structural learning model for joint sentiment classiﬁcation and aspect analysis of text at various levels of granularity. Our model aims to identify highly informative sentences that are aspect-speciﬁc in online custom reviews.
Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achieving inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal relations. We trained machine learning models using features derived from WordNet and the Google N-gram corpus, and they outperformed a variety of baselines, achieving an F-measure of 49.0 for temporals and 52.4 for causals. ...
In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the singlecandidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected.
We introduce an approach to the automatic acquisition of new concepts fi'om natural language texts which is tightly integrated with the underlying text understanding process. The learning model is centered around the 'quality' of different forms of linguistic and conceptual evidence which underlies the incremental generation and refinement of alternative concept hypotheses, each one capturing a different conceptual reading for an unknown lexical item.
We use an EM algorithm to learn user models in a spoken dialog system. Our method requires automatically transcribed (with ASR) dialog corpora, plus a model of transcription errors, but does not otherwise need any manual transcription effort. We tested our method on a voice-controlled telephone directory application, and show that our learned models better replicate the true distribution of user actions than those trained by simpler methods and are very similar to user models estimated from manually transcribed dialogs. ...
We propose a new language learning model that learns a syntactic-semantic grammar from a small number of natural language strings annotated with their semantics, along with basic assumptions about natural language syntax. We show that the search space for grammar induction is a complete grammar lattice, which guarantees the uniqueness of the learned grammar.
We investigate automatic classiﬁcation of speculative language (‘hedging’), in biomedical text using weakly supervised machine learning. Our contributions include a precise description of the task with annotation guidelines, analysis and discussion, a probabilistic weakly supervised learning model, and experimental evaluation of the methods presented. We show that hedge classiﬁcation is feasible using weakly supervised ML, and point toward avenues for future research.
There have been three main reasons for this increase in interest: 1. Scientific adequacy of the models 2. The availability of fine-grained parallel hardware to run the models 3. The demonstration of powerful connectionist learning models. The scientific adequacy of models based on a small number of coarse-grained primitives (e.g. conceptual dependency), popular in AI during the 70's, has been called into question and substantially replaced by a current emphasis in much of computational linguistics on lexicalist models (i.e., ones which use words for representing concepts or meanings).
Nhằm phục vụ cho việc dạy và học mời quý thầy cô, các bạn học sinh tham khảo giáo án của unit 4 Learning a foreign language trong chương trình Tiếng Anh lớp 9. Thông qua những bài soạn giáo án của unit 4 Learning a foreign language giáo viên có nhiều tư liệu tham khảo giúp học sinh làm quen với chủ đề “Học một ngoại ngữ”, thông qua bài học học sinh sẽ có kinh nghiệm hơn trong cách học tiếng Anh.
Giúp giáo viên thuận lợi trong việc thiết kế bài giảng, giới thiệu đến các bạn bộ sưu tập bài giảng unit 4 Learning a foreign language để có tài liệu tham khảo. Đây là những bài giảng được thiết kế bởi các giáo viên kinh nghiệm, bạn có thể an tâm về nội dung bài học, đây sẽ là tài liệu hữu ích giúp bạn củng cố kiến thức cho học sinh. Qua đây, học sinh cũng có thể sử dụng bài giảng để tìm hiểu nội dung bài học.
Analyze tabular data using the BI Semantic Model (BISM) in Microsoft® SQL Server® 2012 Analysis Services—and discover a simpler method for creating corporate-level BI solutions. Led by three BI experts, you’ll learn how to build, deploy, and query a BISM tabular model with step-by-step guides, examples, and best practices. This hands-on book shows you how the tabular model’s in-memory database enables you to perform rapid analytics—whether you’re a professional BI developer new to Analysis Services or familiar with its multidimensional model....
Beginning Blender covers the Blender 2.5 release in-depth. The book starts with the creation of simple figures using basic modeling and sculpting. It then teaches you how to bridge from modeling to animation, and from scene setup to texture creation and rendering, lighting, rigging, and ultimately, full animation. You will create and mix your own movie scenes, and you will even learn the basics of games logic and how to deal with games physics.
Every student of finance or applied economics learns the lessons of Franco Modigliani and Merton Miller. Their landmark paper, published in 1958, laid out the basic underpinnings of modern finance and these two distinguished academics were both subsequently awarded the Nobel Prize in Economics. Simply stated, companies create value when they generate returns that exceed their costs. More specifically, the returns of successful companies will exceed the risk-adjusted cost of the capital used to run the business.
The Matlab programming language provides an excellent introductory language, with built-in graph-ical, mathematical, and user-interface capabilities. The goal is that the student learns tobuild computational models with graphical user interfaces (GUIs) that enable exploration of model behavior.
The next chapter describes how a conventional model of ocean-atmosphere interaction
can be modified to include the free convection within the air and the swell of the
ocean. These effects become particularly important for low wind velocities, where the
standard Charnock wind stress formula leads to a singularity. The article should be of
particular interest to readers who are familiar with standard turbulence modeling, and
it is important to weather modeling.