PPT-NLP Highlights GS Savova

And team Medication CEM template associatedCode Changestatus Conditional Dosage Duration Enddate Form Frequency Generic Negationindicator Route Startdate Strength Subject Uncertaintyindicator. John Blitzer. 自然语言计算组. http://research.microsoft.com/asia/group/nlc/. Why should I know about machine learning? . This is an NLP summer school. Why should I care about machine learning?. By Cindy Carless. 14 Nov 14. What a mouthful…. So much in fact that I thought I had bitten off more than I could chew…. Yet for “Tasting” . LetsTest. it seems very appropriate. Some appetisers to focus on. Detecting Questions in Micro-text. Kyle Dent <. kdent@parc.com. >. Original Question: Do people use Twitter to ask questions?. First Answer: Of course.. But to what extent and what kinds of questions?. Morphology and the Lexicon. Mental Lexicon. What is the meaning of cat? Its pronunciation? Part of speech?. What is the meaning of . wug. ?. What is the meaning of . cluvious. ?. Compare . traftful. and . Semantic Role Labeling. Syntactic Variation. Last week, Min broke the window with a hammer.. The window was broken with a hammer by Min last week. With a hammer, Min broke the window last week. Last week, the window was broken by Min with a hammer. Regina Barzilay. What is NLP?. Goal: intelligent processing of human language. Not just effective string matching. Applications of NLP technology:. Less ambitious (but practical goals): spelling corrections, name entity extraction. SciCOFish Steering Committee meeting. Noumea, March 2015. Coastal fisheries. Provide 50%–90. % of protein intake for coastal . communities. Very important for local incomes (provides around 50% of coastal households with 1. In the Context of the ETC Project. SAW Intro. Wiki. Users collaboratively develop and organize content. Information Analysis is up to the user. Goal: . “Self-aware wiki that can develop and organize its content. Properties of Propositional Logic. Pros. Compositional. Declarative. Cons. Limited expressive power. Represents facts. First Order Logic. Used to represent. Objects – Martin the cat. Relations – Martin and Moses are brothers. Introduction. Polysemy. Words have multiple senses. Example. Let’s have a drink in the bar. I have to study for the bar. Bring me a chocolate bar. Homonymy. May I come in?. Let’s meet again in May. Parser. Earley. parser. Problems with left recursion in top-down parsing. VP . . VP PP. Background. Developed by Jay Earley in 1970. No need to convert the grammar to CNF. Left to right. Complexity. Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing. Dan Jurafsky. Stanford University. Spring 2020 . Introduction and Course Overview. Thanks to . Tsvetkov. and Black course . for ideas and slides!. How should we use NLP for good and not for bad?. The common misconception is that language has to do with . by Hua Xu. Recent Activities. ETL tool development. Note Type Normalization. COVID-19 lab test normalization . A Potential ETL Workflow for NLP. Note. Note_NLP. Measure-. ment. Condition. Procedure. Drug.