FreeMind is the powerful, free mind mapping software used by millions of people worldwide
to capture their ideas and communicate them visually.
Mind mapping with FreeMind will teach you how to develop different kinds of mind maps to
capture and arrange your ideas. You will learn how to combine FreeMind or Freeplane with
other free software in order to enhance the mind maps. You will learn to link and share them
for use with mobile devices.
This provides easy-to-follow instructions to design different types of mind maps according to
the needs of teachers and students.
Designed to help you use more of your brainpower with less effort, an idea map is a colorful visual representation of a particular issue, problem, or idea among other applications on a single piece of paper. Our brains are much better at absorbing, processing, and remembering information presented in the form of an idea map as opposed to information presented in a multi-linear document.
In Learning iOS Game Programming, you’ll learn how to build a 2D tile map game, Sir Lamorak’s Quest: The Spell of Release (which is free in the App Store). You can download and play the game you’re going to build while you learn about the code and everything behind the scenes. Daley identifies the key characteristics of a successful iPhone game and introduces the technologies, terminology, and tools you will use.
To help parents and teachers with more references in the process of teaching English to children, invite you to consult the contents workbooks "The store map" below. Content document provides exercises strengthen your vocabulary. Hope this is useful references for you.
As a language teacher, you can choose the method of teaching English to children with stories in English with vivid visuals. To help you have more material in the process of learning and teaching English, you are invited to refer to the contents of the book "My home map" below.
We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. We develop an algorithm that maintains explicit, lambda-calculus representations of salient discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis.
Build beautiful interactive maps on your Drupal website, and tell engaging visual stories with your data. This concise guide shows you how to create custom geographical maps from top to bottom, using Drupal 7 tools and out-of-the-box modules. You’ll learn how mapping works in Drupal, with examples on how to use intuitive interfaces to map local events, businesses, groups, and other custom data.
We present a system that learns to follow navigational natural language directions. Where traditional models learn from linguistic annotation or word distributions, our approach is grounded in the world, learning by apprenticeship from routes through a map paired with English descriptions. Lacking an explicit alignment between the text and the reference path makes it difﬁcult to determine what portions of the language describe which aspects of the route.
In this paper, we address the task of mapping high-level instructions to sequences of commands in an external environment. Processing these instructions is challenging—they posit goals to be achieved without specifying the steps required to complete them. We describe a method that ﬁlls in missing information using an automatically derived environment model that encodes states, transitions, and commands that cause these transitions to happen.
Compositional question answering begins by mapping questions to logical forms, but training a semantic parser to perform this mapping typically requires the costly annotation of the target logical forms. In this paper, we learn to map questions to answers via latent logical forms, which are induced automatically from question-answer pairs. In tackling this challenging learning problem, we introduce a new semantic representation which highlights a parallel between dependency syntax and efﬁcient evaluation of logical forms. ...
In this paper, we present a reinforcement learning approach for mapping natural language instructions to sequences of executable actions. We assume access to a reward function that deﬁnes the quality of the executed actions. During training, the learner repeatedly constructs action sequences for a set of documents, executes those actions, and observes the resulting reward. We use a policy gradient algorithm to estimate the parameters of a log-linear model for action selection.
A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state. To deal with the high degree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difﬁculty—Robocup sportscasting, weather forecasts (a new domain), and NFL recaps. ...
This paper introduces a machine learning method based on bayesian networks which is applied to the mapping between deep semantic representations and lexical semantic resources. A probabilistic model comprising Minimal Recursion Semantics (MRS) structures and lexicalist oriented semantic features is acquired. Lexical semantic roles enriching the MRS structures are inferred, which are useful to improve the accuracy of deep semantic parsing.
This paper introduces the concepts of asking point and expected answer type as variations of the question focus. They are of particular importance for QA over semistructured data, as represented by Topic Maps, OWL or custom XML formats. We describe an approach to the identiﬁcation of the question focus from questions asked to a Question Answering system over Topic Maps by extracting the asking point and falling back to the expected answer type when necessary.
Spoken language generation for dialogue systems requires a dictionary of mappings between semantic representations of concepts the system wants to express and realizations of those concepts. Dictionary creation is a costly process; it is currently done by hand for each dialogue domain. We propose a novel unsupervised method for learning such mappings from user reviews in the target domain, and test it on restaurant reviews.
We present a new approach for mapping natural language sentences to their formal meaning representations using stringkernel-based classiﬁers. Our system learns these classiﬁers for every production in the formal language grammar. Meaning representations for novel natural language sentences are obtained by ﬁnding the most probable semantic parse using these string classiﬁers. Our experiments on two realworld data sets show that this approach compares favorably to other existing systems and is particularly robust to noise. ...
Often one may wish to learn a tree-to-tree mapping, training it on unaligned pairs of trees, or on a mixture of trees and strings. Unlike previous statistical formalisms (limited to isomorphic trees), synchronous TSG allows local distortion of the tree topology. We reformulate it to permit dependency trees, and sketch EM/Viterbi algorithms for alignment, training, and decoding.
We present a connectionist architecture and demonstrate that it can learn syntactic parsing from a corpus of parsed text. The architecture can represent syntactic constituents, and can learn generalizations over syntactic constituents, thereby addressing the sparse data problems of previous connectionist architectures. We apply these Simple Synchrony Networks to mapping sequences of word tags to parse trees.
PCFGs can be accurate, they suffer from vocabulary coverage problems: treebanks are small and lexicons induced from them are limited. The reason for this treebank-centric view in PCFG learning is 3-fold: the English treebank is fairly large and English morphology is fairly simple, so that in English, the treebank does provide mostly adequate lexical coverage1 ; Lexicons enumerate analyses, but don’t provide probabilities for them; and, most importantly, the treebank and the external lexicon are likely to follow different annotation schemas, reﬂecting different linguistic perspectives.