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深度学习驱动的自然语言处理实战MEAP

DeepLearning

NLP

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"Deep Learning for Natural Language Processing MEAP" 是一本专为深度学习在自然语言处理领域的应用提供实用指南的电子书籍。MEAP版本（Manning Early Access Program）允许读者从曼宁账户下载最新版本，该账户网址是account.manning.com。购买此书的读者可享受到客户服务，如有任何关于书稿的问题或反馈，如非排版错误，可以通过support@manning.com联系。本书的核心内容覆盖了将深度学习技术应用于当前人工智能中最活跃的应用之一——自然语言分析。它旨在帮助开发者、学生以及计算语言学研究者掌握这些前沿技术。虽然其基础建立在语言学，即计算语言学或简称NLP上，但书中涉及的工具和技术借鉴了计算机科学、逻辑、统计学和机器学习等多个邻近领域。书中深入探讨了诸如词嵌入（word embeddings）、循环神经网络（Recurrent Neural Networks, RNNs）、长短时记忆网络（LSTM）、Transformer模型等关键深度学习架构在文本分类、情感分析、机器翻译、语义理解等NLP任务中的应用。此外，还将介绍如何处理自然语言的复杂性，如语法、语义和上下文理解，并提供实际项目案例，以便读者能将理论知识转化为实践操作。作为MEAP版本，读者可以在购买后实时获取到作者的最新更新和修订，参与到内容的完善过程中。论坛地址<https://forums.manning.com/forums/deep-learning-for-natural-language-processing>，读者可以在这里分享见解，与其他学习者交流经验，共同进步。 "Deep Learning for Natural Language Processing MEAP"是一本实用且紧跟潮流的教材，适合那些希望在自然语言处理领域利用深度学习技术解决问题的专业人士和爱好者。通过阅读本书，读者不仅能提升技能，还能了解到深度学习在NLP领域的最新进展和发展趋势。

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The reverend had a heart attack in church => sci.med

Apparently, these few short texts can be linearly separated by a

simple, weight-based algorithm. This example is a huge

simpliﬁcation: the topics chosen are quite distinct. In real life, linear

algorithms fall short in separating topics that overlap and share

similar vocabulary.

The multilayer perceptron generalizes the single-layer model of the

original perceptron to a multilayer model, with at least 3 layers: an

input layer, one or more hidden representational layers, and an

output layer:

Figure 1.9. A multilayer perceptron with one hidden layer (

…h

As mentioned, machine learning algorithms that perform

classiﬁcation (the labeling of objects with classes) attempt to arrive

at linear boundaries between data points. Recall our

_linear_classiﬁer for such a linear boundary. Imagine you are seeing

two objects (like an orange and an apple) on a plate, one of which

1.2.2 Support Vector Machines

is partially obscuring the other. If you close one eye, you will lose

3D stereovision, and you cannot separate the two objects in 2D. If

you open both eyes, you will be able to separate the two objects.

Support Vector Machines routinely perform this migration to a

higher dimension, separating objects in this space. Their secret

weapon:

kernels

Figure 1.10. From 2D to 3D with a kernel.

A support vector machine is a binary classiﬁer that implicitly maps

data in feature space to higher dimensions in which data becomes

separable by a linear plane, called a

hyperplane

. This mapping is

implicit, and is carried out by a

kernel function

. This is a function

that transforms the original input space to an alternative

representation that implicitly has a higher dimensionality, with the

aim of disentangling the data and making it linearly separable.

But the migration is implicit in the sense that it takes the form of a

similarity function (φ in the picture above) applied to two feature

vectors, just computing their distance. This is cordially called the

kernel trick

, It sounds like sheer magic, but it is actually quite

simple. Let’s take a look.

You should already be familiar with the dot product of two vectors.

If not, please see Appendix 2 for a refresher. To recap, the standard

dot product of two vectors

and

is the sum of the cross-product

of the two vectors:

Listing 1.2. Dot product in Python.

def dot_product(a,b):

return sum( [a[i]*b[i] for i in range(len(a))])

So, a dot product is just a multiplicative operation on two vectors

that produces a single number.

Kernels are generalizations of this dot product between vectors:

they compute the dot product between

altered

versions of these

vectors. The nature of alteration is speciﬁed by a

kernel function

Generally speaking, a kernel function takes two vectors, mixes in a

constant (a kernel parameter) and adds some kernel-speciﬁc

ingredients to produce a speciﬁc form of a dot product of the two

vectors.

Let’s return to our orange and apple. The objects are described by

pairs of coordinates (x,y), since the table they’re lying on is a ﬂat

XY-plane. Like othertypes of kernels, the so-called

polynomial

kernel

maps lower-dimensional spaces to higher-dimensional ones.

You may recall from high school math that a polynomial function

produces a value using addition, subtraction, multiplication or

positive exponentiation only. Like

y=4x

+10

. Polynomial kernels

work on two input values (vectors of numeri values)and usually a

constant. They compute a result using a polynomial.

For instance, a simple

quadratic

kernel

, using a constant

, and

addressing the two two-dimensional XY-vectors describing our

orange and apple:

x=(

)

and

y=(

)

looks like:

Figure 1.11. Quadratic kernel.

Notice the subscripted T? That is vector transposition (see

Appendix B), necessary for vector multiplication. What does the

kernel do? It computes a product between two vectors. This product

is a number expressing a relation between the two input vectors.

But the tedious expansion of this kernel shows that we actually

operate in a six- (and not even a three-!) dimensional space: just

count the factors separated by the plus sign in the result; we have

six such factors. In this case, the kernel function

implicitly

computes its dot product between the following vectors:

<c,x

,√2x

<c,y

,√2y

since the result is the product of these two vectors. But it never

explicitly created these vectors. The whole point of this

kernel trick

is that, hopefully, in the higher-dimensional space that is the

playground of the kernel, things become easier separable than in

the entangled input space. Kernels do not explicitly represent this

space, they implicitly work in it. You can imagine that, for long

vectors and large exponents of the polynomial, this kernel trick

quickly becomes a practical advantage!

In the transformed space created by the kernel trick, two classes

are at best separated with maximally wide boundaries (called

maximum margins

). The data points determining the slope of these

boundaries are called

support vectors

Figure 1.12. Maximum margins of an SVM. The support vectors are the points

on the dashed lines.

Learning weights that optimize the margins with the least error (as

measured on some held-out test data) is the job an SVM has to

solve during training. After training, the support vectors and the

various weights plus biases constitute the model. New input is

projected onto the support vectors, and depending on which side it

lands, it receives a positive or negative label (recall that SVM’s are

binary classiﬁers). So, SVMs throw away a lot of their training data,

only keeping some of it: the support vectors. They can be called

eager

forms of machine learning.

The connection between kernel-based machine learning and neural

network-based learning is brieﬂy discussed in the

Deep Learning

with Python

book. It is based on the intuition that hidden layers in

neural networks act like kernels in disentangling linearly

inseparable input data layer-by-layer, step-by-step.

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