錄播問(wèn)答系統(tǒng)詞向量句子倒排表項(xiàng)目作業(yè)解釋-課程內(nèi)容lesson

開(kāi)始：文本表示每個(gè)單詞的表示：：爬山：跑步:昨天：Word

Representation詞典：

[

，去，爬山，今天，

，昨天，跑步]每個(gè)句子的表示今天去爬山：昨天跑步：又去爬山又去跑步：Sentence

Representation

(boolean)詞典：

[

，又，去，爬山，今天，

，昨天，跑步]每個(gè)句子的表示今天去爬山：昨天跑步：又去爬山又去跑步：Sentence

Representation

(count)詞典：

[

，又，去，爬山，今天，

，昨天，跑步]結(jié)束：文本表示開(kāi)始：文本相似度S1:

“S2:

"S3:今天去爬山”=(1,0,1,1,0,0,0,0)昨天跑步"=(0,0,0,0,0,1,1,1)又

去

爬山

又

去

跑步 "

=

(0,2,2,1,0,1,0,1)“Sentence

Similarity計(jì)算距離（歐式距離）：??=|?????????|Sentence

Similarity計(jì)算相似度（余弦相似度）：??

=

????

/????/(

????

? ????

)S1:

“S2:

"S3:今天去爬山”=(1,0,1,1,0,0,0,0)昨天跑步"=(0,0,0,0,0,1,1,1)又

去

爬山

又

去

跑步 "

=

(0,2,2,1,0,1,0,1)“Sentence

Similarity句子1：He

is

going

from

Beijing

to

Shanghai句子2：He

denied

my

request,but

he

actually

lied.句子3：Mike

lost

the

phone,and

phone

was

in

the

car句子1：(0,0,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,1,0,1,0)句子2：(1,0,0,1,0,1,0,0,2,0,0,1,0,0,1,0,1,0,0,0,0)句子3：(0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,2,0,0,2,0,1)deniedheSentence

Similarity句子1：He

is

going

from

Beijing

to

Shanghai句子2：He

denied

my

request,but

he

actually

lied.句子3：Mike

lost

the

phone,and

phone

was

in

the

car句子1：(0,0,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,1,0,1,0)句子2：(1,0,0,1,0,1,0,0,2,0,0,1,0,0,1,0,1,0,0,0,0)句子3：(0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,2,0,0,2,0,1)deniedhe并不是出現(xiàn)的越多就越重要！并不是出現(xiàn)的越少就越不重要！結(jié)束：文本相似度開(kāi)始：tf-idf

文本表示Sentence

Similarity句子1：He

is

going

from

Beijing

to

Shanghai句子2：He

denied

my

request,but

he

actually

lied.句子3：Mike

lost

the

phone,and

phone

was

in

the

car句子1：(0,0,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,1,0,1,0)句子2：(1,0,0,1,0,1,0,0,2,0,0,1,0,0,1,0,1,0,0,0,0)句子3：(0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,2,0,0,2,0,1)deniedhe并不是出現(xiàn)的越多就越重要！并不是出現(xiàn)的越少就越不重要！Tf-idf

Representation??????????(??)=

????

??,

??

?

??????(??)文檔??中??的詞頻??????????(??)N:語(yǔ)料庫(kù)中的文檔總數(shù)N(w):詞語(yǔ)w出現(xiàn)在多少個(gè)文檔？??????????(??)=

????

??,

??

?

??????(??)今天上NLP課程

今天的課程有意思數(shù)據(jù)課程也有意思Measure

Similarity

Between

Words下面哪些單詞之間語(yǔ)義相似度更高？，爬山，運(yùn)動(dòng)，昨天結(jié)束：tf-idf文本表示開(kāi)始：詞向量介紹Measure

Similarity

Between

Words下面哪些單詞之間語(yǔ)義相似度更高？，爬山，運(yùn)動(dòng)，昨天Measure

Similarity

Between

Words利用One-hot表示法表達(dá)單詞之間相似度？每個(gè)單詞的表示：：

[1,

0,

0,

0,

0,

0,

0]爬山：

[0,

0, 1,

0,

0,

0,

0]運(yùn)動(dòng):

[0,

0,

0,

0,

0,

0,

1]昨天：

[0,

0,

0,

0,

0,

1,

0]Another

Issue:

Sparsity今天打算去爬山昨天做什么了明天打算去上課From

One-hot

Representation

toDistributed

Representation：

[1,

0,

0,

0,

0,

0,

0]爬山：

[0,

0, 1,

0,

0,

0,

0]運(yùn)動(dòng):

[0,

0,

0,

0,

0,

0,

1]昨天：

[0,

0,

0,

0,

0,

1,

0]：

[0.1,

0.2,

0.4,

0.2]爬山：

[0.2,

0.3,

0.7,

0.1]運(yùn)動(dòng):

[0.2,

0.3,

0.6,

0.2]昨天：

[0.5,

0.9, 0.1,

0.3]One-Hot

RepresentationDistributed

RepresentationMeasure

Similarity

Between

Words：

[0.1,

0.2,

0.4,

0.2]爬山：

[0.2,

0.3,

0.7,

0.1]運(yùn)動(dòng):

[0.2,

0.3,

0.6,

0.2]昨天：

[0.5,

0.9, 0.1,

0.3]Distributed

RepresentationComparing

the

CapacitiesQ:100維的One-Hot

表示法最多可以表達(dá)多少個(gè)不同的單詞？Q:100維的分布式表示法最多可以表達(dá)多少個(gè)不同的單詞？Comparing

the

CapacitiesQ:100維的One-Hot

表示法最多可以表達(dá)多少個(gè)不同的單詞？Q:100維的分布式表示法最多可以表達(dá)多少個(gè)不同的單詞？QuestionsQ:怎么學(xué)習(xí)每一個(gè)單詞的分布式表示（詞向量）？結(jié)束：詞向量介紹開(kāi)始：學(xué)習(xí)詞向量Learn

Word

Embeddings今天去爬山你么昨天運(yùn)動(dòng)去爬山：

[0.1,

0.2,

0.4,

0.2]爬山：

[0.2,

0.3,

0.7,

0.1]運(yùn)動(dòng):

[0.2,

0.3,

0.6,

0.2]昨天：

[0.5,

0.9, 0.1,

0.3]Distributed

RepresentationEssence

of

Word

Embedding：

[0.1,

0.2,

0.4,

0.2]爬山：

[0.2,

0.3,

0.7,

0.1]運(yùn)動(dòng):

[0.2,

0.3,

0.6,

0.2]昨天：

[0.5,

0.9, 0.1,

0.3]Distributed

RepresentationFrom

Word

Embedding

to

SentenceEmbedding結(jié)束：學(xué)習(xí)詞向量開(kāi)始：基于檢索的問(wèn)答系統(tǒng)缺點(diǎn)QuestionHow

do

you

like

NLPCamp?<Question1，answer1><question2，answer2>……<question100，answer100>a返回相似度最高的相似度匹配Recap:

Retrieval-based

QA

System思路：“層次過(guò)濾思想”How

to

Reduce

Time

Complexity?Recap:

Retrieval-based

QA

SystemQuestion“How

do

you

like

NLPCamp?”<question1，answer1><question2，answer2>……<question100，answer100>返回相似度最高的過(guò)濾相似度匹配<question2，answer2><question17，answer17>……<question98，answer9