深度语法分析在自然语言的应用

1、深度语法分析在自然语言的应用技术创新，变革未来Outline人工智能的历史和现状简介：从感知到认知此消彼长的经验主义和理性主义钟摆深度解析(Deep Parsing)是什么？NLP 架构纵览核武器应用举例Outline: AI History人工智能的历史和现状简介：从感知到认知此消彼长的经验主义和理性主义钟摆NLP Mainstream Since 1990sCourtesy of Prof. Church: “A Pendulum Swung Too Far”/blog-362400-988692.htmlTwo Basic Approaches to NLPComplementary r

2、ather than competingHybrid: Best of both worldsBalance and configurability between precision and recallGrammar Engineering(based onsentence structure)Good for sentence levelHandles short messages wellHigh precisionFine-grained insightsEasy to debugParsing and understandingRequires deep skillsRequire

3、s scale up skillsRequires robustness skillsModerate recall (coverage)Parser development slowApproachProsConsStatistical Learning(based on keywords)Good for document-levelHigh recallRobustEasy to scaleFast development (if data available)Requires large annotationCoarse-grainedDifficult to debugFail in

4、 short messagesOnly shallow NLPNo understandingOutline: What Is Deep Parsing人工智能的历史和现状简介：从感知到认知此消彼长的经验主义和理性主义钟摆深度解析(Deep Parsing)是什么？Deep Parsing: Unstructured to StructuresWhy parsing?Limited PatternsSubtree Pattern: Data to IntelligenceSVO Paern:Barack Obama (S) Endorse (V) Hillary Clinton (O)Know

5、ledge GraphDeep Parsing: Unstructured to StructuresSubtree Pattern: Data to IntelligenceInter-Clause Paern:虽然 遗憾无所谓Linear: Innite number of sentencesStructure:Limited paernsmild senGmentData IntelligenceOutline: NLP Architectures人工智能的历史和现状简介：从感知到认知此消彼长的经验主义和理性主义钟摆深度解析(Deep Parsing)是什么？NLP 架构纵览NLP Ar

6、chitecture 1: Deep Parser as CoreCascaded FSAs break through Chomskys hierarchy walls Robust, linear, F-measure: scale up to big dataSample Deep Parse Tree (dependency)Sample Deep Parse Tree (PS flavor)NLP Architecture 2: Information ExtractionIncluding sentiment analysis (on subjective language)NLP

7、 Architecture 3: Text MiningNLP Architecture 4: Landing on ApplicationsSample Deep Parse TreeSample Deep Parse TreeOutline: NLP Applications人工智能的历史和现状简介：从感知到认知此消彼长的经验主义和理性主义钟摆深度解析(Deep Parsing)是什么？NLP 架构纵览核武器应用举例社媒舆清分析，大数据挖掘，智能搜索，对话系统 Sentiment AnalysisWhy deep parsing, not deep learning?Learning wi

8、thout parsing does not work for social media sentimentSocial media is dominated by short messagesStatistical learning breaks in short messages: no sufficient data pointsDeep parsing enables linguistic analysis for best precisionDeep parsing enables insights mining 2 magnitudes more efficientparsing-

9、supported rule has power of about 100 ngram rulesDeep learning is a great algorithm but still delinked from parsingParsers trained by deep learning are all research systemsdifficult to adapt to real life text of social media (or other genres)knowledge bottleneck: domains where labeled data are insuf

10、ficientReal life deep learning systems are mostly end-to-end, still no structuresanalysisSentiment Analysis: Bag of Words vs. ParsingKEYWORD CHALLENGEThe iPhone has never been good.The iPhone has never been this good.ASSOCIATION CHALLENGEAnother reason to switch from Visa to MasterCard I prefer Mast

11、erCard over Visa.MasterCard is way better than Visa.CLASSIFICATION CHALLENGEI had a wonderful day today. Even my instant coffee tastes great. However my Dell laptopdoesnt boot again. Maybe I should check out the MacBook. It MacBook seems so easy to use.FCionaer-sgera-ginraeidneAdnaClylassissiuficnac

12、toiovnertshu“wmhbys”-buephainndd sdeonwtinm: eonvtesr:all tone positive (3 vs 1)Instant coffee / tastes greatDell Laptop / does not bootMacbook / easy to useDeep Parsing Supports Deep SentimentsSentiment analysis has different layerssentiment classification: thumbs-up and down (or neutral)sentiment

13、association: to associate a sentiment with a topic or brand as its objectdeep sentiment insights:who has the sentiment?how intense?why?Evaluations, comparisons and contrasts;needs and wish-list;positive/negative actions (e.g. adopt / abandon);purchase intent;pros and consMost learning systems stop a

14、t 1 and sometimes at 2.All 3 can be done via deep parsing.Illustration: Real-time PollsChallenges observed:economy topicat 6:55pm;China topic at 7:30pmIllustration: Stock Market TrendsTopic: HTCData 1: Stock Market PerformanceData 2:Chinese social media (Weibo, Tianya, Facebook, Twitter)Time range:2

15、013/08 2014/08Strong correlation observedBig Data Mining: Who benefits?For businesses: social listeningConsumer in:sights: sentiments and why Brand image: trendsCompetitive research: where do we standFor consumersPurchase decision Personalized serviceFor governmentElection campaignPublic opinions on

16、 policies and social topicsOthers?Hot topics or anywhere public opinions are involved Stock market trends correlationFor consumers: Purchase DecisionFor consumers: Purchase DecisionFor consumers: Purchase DecisionIntelligent Search and ChatbotsA mixture/convergence of 3 is possibleThree types of Cha

17、tbots:Domain knowledge QA:e.g. customer service;Open domain knowledge QA:e.g. Who won Nobel Prize in 2015?Interactive chatting: e.g. just for fun (killing time);in time, for comfort (senior people); for mental health counsellingQ:questions are a subset of language, tractablefor decoding intent, asking p