Automatic Keyphrase Extraction by Bridging Vocabulary Gap

Xinxiong ChenTsinghua University

2013-04-26

04/19/2023 THUNLP, Tsinghua University http://nlp.csai.tsinghua.edu.cn

1

Main Idea Vocabulary gap: Appropriate keyphrases

are not always statistically significant or even do not appear in the given document.

Use word alignment models in statistical machine translation to learn translation probabilities between the words in documents and the words in keyphrases.

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 2

Introduction – Keyphrase What is keyphrase

a set of terms selected from a document as a short summary of the document.

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 3

Introduction – Keyphrase Extraction

Why keyphrase extraction Digital libraries Information Retrieval

Goal : automatically extract keyphrases from documents Unsupervised

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 4

Example A News article: (translated from

Chinese)

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 5

Title Israeli Military Claims Iran Can Produce Nuclear Bombs and Considering Military Action against Iran

Summary …

Content …

Keywords Israeli , Iran , Nuclear bombs , Nuclear weapon

Example

Existing unsupervised method: TFIDF : Nuclear bombs , Iran , Israeli ,

enriched uranium , speech TextRank : Iran , Israeli , chief , Nuclear

bombs , Military Use a window whose size is a constant to build a word graph Use PageRank to decidewhich word is more important

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 6

TF Israeli(6) Iran(6) intelligence(5) nuclear bombs(4) enriched uranium(3)…nuclear weapon(1)

Example LDA : Iran , England , America , Nation

, Speech Learn topics from documents

ExpandRank : Iran , enriched uranium , Israeli , atomic energy, Lebanon Find k nearest neighbor documents to

build word graphs

04/19/2023 THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

7

Idea - Association If a word is mentioned, it remind people

of other words. iPhone – Apple Nuclear bombs – Nuclear Weapon

What is the probability between “Nuclear bombs” and “Nuclear Weapon”?

04/19/2023 THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

8

Nuclear bombs Nuclear Weapon

Idea – SMT for Keyphrase Extraction

Both the content and the keyphrase are parallel summaries of a news

Unsupervised : Use title or summarization instead

Estimate the translation probabilities between the words in content and title word alignment models

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

News

Content Title(Summarization)Translation

04/19/2023 9

Translation Probability Example:

Nuclear bombs: Nuclear bombs : 0.515757 Liquid : 0.0871815 Nuclear Weapon : 0.0808868 Military Action : 0.0239178 Israeli Military : 0.0215988 Miniaturization : 0.0118 Possible : 0.0113688 enriched uranium : 0.0100252

04/19/2023 THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

10

Keyphrase Extraction Using WAM

Given news, rank keyphrases by computing the scores

Iran , Israeli , chief, Nuclear bombs , Military …

Iran , Israeli , chief, Nuclear bombs , Nuclear weapon , Military , speech

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 11

Word Trigger Method (WTM) Three Steps :

Preparing translation pairs Learning a translation model

IBM Model-1 Extracting keyphrase given a resource

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 12

Translation Pairs Length unbalance problem

Unable to list all tags on the annotation side

Tags may have different importance for the resource

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 13

Content-Title Pairs Length unbalanced problem

Unable to list all tags on the annotation side

Tags may have different importance for the resource

Sampling Method Tag weighting type

TFt, TF-IRFt

Length ratio

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 14

Learning Translation Probabilities IBM Model-1 as WAM algorithms

Asymmetric: Prd2a(t|w), Pra2d(t|w) Linear Combination

Prd2a(t|w)

Pra2d(t|w) When λ = 1 or λ = 0, it simply uses model

Prd2a(t|w) or Pra2d(t|w) correspondingly

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 15

content title

title content

Tag Suggestion Using Triggered Words

Given description, rank tags by computing the scores

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 16

Tag Suggestion Using Triggered Words

Given description, rank tags by computing the scores

Trigger power of the word w in the content TF-IRFw

TextRank Their product

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 17

Keyphrase Extraction Using Triggered Words

Given description, rank tags by computing the scores

Translation probabilities from words in description to keyphraes

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 18

Emphasize Tags Appearing In Content for WTM (EWTM)

Emphasize tags appearing in description

It(w): indicator function to emphasize the tags appearing in content Gets 1 when t = w Gets 0 when t != w

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 19

Experiments Datasets

13702 news from www.163.com

Evaluation Metrics Precision, recall and F-measure

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 20

Words in documents 72900

Words in keyphrases 12405

Lengths of document 971.7 words

Lengths of titles 11.6 words

Lengths of summarization 45.8 words

Num of Keyphrases 2.4

Experiment Results

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 21

Parameters – Length Ratio The length ratio: content/title

04/19/2023 THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

22

THUNLP, Tsinghua Universityhttp://nlp.csai.tsinghua.edu.cn

04/19/2023 23

SINA APP(http://app.thunlp.org/weibo)Now we have more than 2 million registered users

Application

Thank you ! Q & A