Building Structured Databases of Factual Knowledge from Massive Text Corpora

Part I: Quality Phrase Mining

Effort-Light StructMine: Methodology

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Data-driven textsegmentation

(SIGMOD’15, WWW’16)

Entity names& context units

Partially-labeledcorpus

Corpus-specificStructureDiscovery

(KDD’15, KDD’16,EMNLP’16, WWW’17)

Structures fromthe remainingunlabeled data

Knowledgebases

Textcorpus

Quality Phrase Mining• Quality phrase mining seeks to extract a

ranked list of phrases with decreasing quality from a large collection of documents• Examples:

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ScientificPapers

NewsArticles

Expected Results

USPresidentAndersonCooperBarack Obama…Obama administration…atown…

Expected Results

data miningmachinelearninginformationretrieval…support vectormachine…the paper…

Why Phrase Mining?

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w/o phrase mining w/ phrase mining• What is “united”?• Which Dao?

• United Airline!• David Dao!

• Applications in NLP, IR, Text Mining• Documentanalysis• Indexinginsearchengine

• Keyphrases fortopicmodeling• Summarization

What Kind of Phrases Are of “High Quality”?• Popularity

• “informationretrieval”>“cross-languageinformationretrieval”

• Concordance• “strongtea”>“powerfultea”• “activelearning”> “learningclassification”

• Informativeness• “thispaper”(frequentbutnotdiscriminative,notinformative)

• Completeness• “supportvectormachine” >“vectormachine”

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Three Families of Methods

Supervised(linguisticanalyzers)

Unsupervised(statistical signals)

Weakly/DistantlySupervised

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Supervised Phrase Mining• Phrase mining was originated from the NLP

community• How to use linguistic analyzers to extract phrases?

• Parsing(e.g.,stanford NLPparsers)• NounPhrase(NP)Chunking

• How to rank extracted phrases?• C-value[Frantzi etal.’00]• TextRank [Mihalcea etal.’04]

• TF-IDF

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• Minimal Grammatical Segments ó Phrases

• Phrases: “the chef”, “the soup”

Linguistic Analyzer – Parsing

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Rawtextsentence(string)

Fullparsetree(grammaticalanalysis)

Thechefcooksthesoup.

Full-textParsing

Linguistic Analyzer – Chunking

• Noun phrase chunking is a light version of parsing

1. Apply tokenization and part-of-speech (POS) tagging to each sentence

2. Search for noun phrase chunks

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Inefficiencies of Linguistic Analyzer• Difficult to directly apply pre-trained to new

domains (e.g. twitter, biomedical, yelp)• Unlesssophisticated,manuallycurated,domain-specifictrainingdataareprovided

• Computationally slow.• Cannotbeappliedonweb-scaledatatosupportemergingapplications

• Lack of the usage of corpora-level information• NPsometimescan’tmeettherequirementsofqualityphrases

• We need “shallow” phrase mining techniques

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Ranking• C-Value• Prefers“maximal”phrases• Popularity&Completeness

• TextRank• SimilartoPageRank• Popularity&Informativeness

• TF-IDF• TermFrequency• InverseDocumentFrequency• Popularity&Informativeness

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Compatibilityofsystemsoflinearconstraintsover

thesetofnaturalnumbers.Criteriaof

compatibilityofasystemoflinearDiophantine

equations,strictinequations,andnonstrict

inequations areconsidered.…..

Three Families of Methods

Supervised(linguisticanalyzers)

Unsupervised(statistical signals)

Weakly/DistantlySupervised

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Unsupervised Phrase Mining

• Statistics based on massive text corpora• Popularity• Rawfrequency• FrequencydistributionbasedonZipfian ranks[Deane’05]

• Concordance• Significancescore[Churchetal.’91][El-Kishky etal.’14]

• Completeness• Comparisontosuper/sub-sequences[Parameswaran etal.’10]

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Raw Frequency• Raw frequency could NOT reflect the quality of

phrases, because• Combine with topic modeling

• Mergeadjacentunigramsofthesametopic[Blei &Lafferty’09]• Frequentpatternminingwithinthesametopic[Danilevsky etal.’14]

• Limitations• Tokensinthesamephrasemaybeassignedtodifferenttopics• E.g.knowledge discovery usingleastsquaressupportvectormachineclassifiers…

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Frequency Distribution• Idea: ranks in a Zipfian frequency distribution is

more reliable than raw frequency• Heuristic: Actual Rank / Expected Rank• Example:• Givenaphraselike“eastend”• ActualRank:rank“eastend”amongalloccurrencesof“east”(e.g.,“east end”,“east side”,“theeast”,“towardstheeast”,etc.)• ExpectedRank:rank“__end”amongallcontextsof“east”(e.g.,“__end”,“__side”,“the__”,“towardsthe__”,etc.)

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Significance score • Significance score [Church et al.’91]• A.k.a.Zscore

• ToPMine [El-Kishky et al.’15]• Ifaphrasecanbedecomposedintotwoparts

• P = P1 ! P2• α(P1,P2)≈(f(P1●P2)̶µ0(P1,P2))/√f(P1●P2)

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Qualityphrases

Significance score (cont’d)• Merge adjacent unigrams greedily if their

significance score is above the threshold.

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Comparison to super/sub-sequences• Frequency ratio between an n-gram phrase

and its two (n-1)-gram phrases• Example

• Pre-confidence ofSanAntonio:2385/14585• Post-confidence ofSanAntonio:2385/2855

• Expand / Terminate based on thresholds

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Phrase Rawfrequency

San 14585

Antonio 2855

SanAntonio 2385

Comparison to super/sub-sequences (cont’d)• Assumption

• Anti-example• “relationaldatabasesystem”isaqualityphrase.• Both“relationaldatabase”and“databasesystem”canbequalityphrases.

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Ann-gramqualityphrase

Two(n-1)-gramsub-phrases

Atleastoneofthemisnotaqualityphrase.

Limitations of Statistical Signals

• The thresholds should be carefully chosen.• Only consider a subset of quality phrase

requirements.• Combining different signals in an

unsupervised manner is difficult.• Introducesomesupervisionmayhelp!

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Three Families of Methods

Supervised(linguisticanalyzers)

Unsupervised(statistical signals)

Weakly/DistantlySupervised

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Weakly / Distantly Supervised Phrase Mining Methods• SegPhrase [Liu et al.’15]• Weaklysupervised

• AutoPhrase [Shang et al.’17]• Distantlysupervised

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SegPhrase

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Document 1Citationrecommendationisaninterestingbutchallengingresearchproblemindataminingarea.

Document 2Inthisstudy,weinvestigatetheprobleminthecontextofheterogeneousinformationnetworksusingdataminingtechnique.

Phrase Mining

Document 3PrincipalComponentAnalysisisalineardimensionalityreduction technique commonly usedin machine learning applications.

Quality Phrases

PhrasalSegmentation

RawCorpus SegmentedCorpus

InputRawCorpus Quality Phrases SegmentedCorpus

• Outperform all above methods on domain-specific corpus (e.g., Yelp reviews)

Quality Estimation• Weakly Supervised

• Labels:Whetheraphraseisaqualityoneornot• “support vector machine”: 1• “the experiment shows”: 0

• For~1GBcorpus,only300labels

• Pros• Binaryannotationsareeasy

• Cons• Theselectionofhundredsofvarying-qualityphrasesfrommillionsofcandidatesshouldbecareful.

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Phrasal Segmentation• Phrasal segmentation can tell which phrase is

more appropriate• Ex:Astandard⌈featurevector⌋ ⌈machinelearning⌋ setupisusedtodescribe...

• Effects on quality re-estimation (real data)• nphardinthestrongsense• nphardinthestrong• databasemanagementsystem

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Notcountedtowardstherectifiedfrequency

Interesting Phrases Mined (From Titles & Abstracts of SIGMOD/SIGKDD Proceedings)

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AutoPhrase• No label selection and annotation effort• Smoothly support multiple languages

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How to get rid of human effort?

• Basic Idea:• Knowledgebasescangiveusacleanpositivepool• Theremainingfrequentn-gramsformanoisynegativepool.However,theratiooffalsenegativeislow.• Ensemble:averagethepredictionsfrombaseclassifiers

• Independence helps to denoise

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AutoPhrase’s Example Results

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ReferencesDeane, P., 2005, June. A nonparametric method for extraction of candidate phrasal terms. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 605-613). Association for Computational Linguistics.

Koo, T., Carreras Pérez, X. and Collins, M., 2008. Simple semi-supervised dependency parsing. In 46th Annual Meeting of the Association for Computational Linguistics (pp. 595-603).

Xun, E., Huang, C. and Zhou, M., 2000, October. A unified statistical model for the identification of English baseNP. In Proceedings of the 38th Annual Meeting on Association for Computational Linguistics (pp. 109-116). Association for Computational Linguistics.

Zhang, Z., Iria, J., Brewster, C. and Ciravegna, F., 2008, May. A comparative evaluation of term recognition algorithms. In LREC.

Park, Y., Byrd, R.J. and Boguraev, B.K., 2002, August. Automatic glossary extraction: beyond terminology identification. In Proceedings of the 19th international conference on Computational linguistics-Volume 1 (pp. 1-7). Association for Computational Linguistics.

Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C. and Nevill-Manning, C.G., 1999, August. KEA: Practical automatic keyphrase extraction. In Proceedings of the fourth ACM conference on Digital libraries (pp. 254-255). ACM.

Liu, Z., Chen, X., Zheng, Y. and Sun, M., 2011, June. Automatic keyphrase extraction by bridging vocabulary gap. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning (pp. 135-144). Association for Computational Linguistics.

Evans, D.A. and Zhai, C., 1996, June. Noun-phrase analysis in unrestricted text for information retrieval. In Proceedings of the 34th annual meeting on Association for Computational Linguistics (pp. 17-24). Association for Computational Linguistics.

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ReferencesFrantzi, K., Ananiadou, S. and Mima, H., 2000. Automatic recognition of multi-word terms:. the c-value/nc-value method. International Journal on Digital Libraries, 3(2), pp.115-130.

Mihalcea, R. and Tarau, P., 2004, July. TextRank: Bringing order into texts. Association for Computational Linguistics.

Blei, D.M. and Lafferty, J.D., 2009. Topic models. Text mining: classification, clustering, and applications, 10(71), p.34.

Danilevsky, M., Wang, C., Desai, N., Ren, X., Guo, J. and Han, J., 2014, April. Automatic construction and ranking of topical keyphrases on collections of short documents. In Proceedings of the 2014 SIAM International Conference on Data Mining (pp. 398-406). Society for Industrial and Applied Mathematics.

Church, K., Gale, W., Hanks, P. and Hindle, D., 1991. Using statistics in lexical analysis. Lexical acquisition: exploiting on-line resources to build a lexicon, 115, p.164.

El-Kishky, A., Song, Y., Wang, C., Voss, C.R. and Han, J., 2014. Scalable topical phrase mining from text corpora. Proceedings of the VLDB Endowment, 8(3), pp.305-316.

Parameswaran, A., Garcia-Molina, H. and Rajaraman, A., 2010. Towards the web of concepts: Extracting concepts from large datasets. Proceedings of the VLDB Endowment, 3(1-2), pp.566-577.

Liu, J., Shang, J., Wang, C., Ren, X. and Han, J., 2015, May. Mining quality phrases from massive text corpora. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 1729-1744). ACM.

Shang, J., Liu, J., Jiang, M., Ren, X., Voss, C.R. and Han, J., 2017. Automated Phrase Mining from Massive Text Corpora. arXiv preprint arXiv:1702.04457.

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