Middeware2012 crowd from mjfrankli

CrowdDB: Answering Queries with Crowdsourcing from Viviana Murello

Inverted list

Sample document collection

Id Contents
1. The only way not to think about money is to have a great deal of it.
2. When I was young I thought that money was the most important thing in life; now that I am old I know that it is.
3. A man is usually more careful of his money than he is of his principles.
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The indexes are:
List 1 Just the document lists. The format is (d1, d2, . . .), where dn is the document id number.
List 2 Document lists with word frequencies. The format is (d1:f1, d2:f2, . . .), where dn is the document id number and fn is the word frequency.
List 3 Document lists and word positions with word granularity. The format is (d1:(w1, w2, . . .), (d2:(w1, w2, . . .), . . .), where dn is the document id number and wn are the word positions.
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MOCVD薄膜之沉積

薄膜之沉積為一表面反應，其沉積速率主要與化學反應動力學有關，相關參數為基板溫度、腔體之操作壓力及反應氣體之成分等
基板溫度屬高溫區，表面化學反應速率高，反應成分之質量傳輸率限制磊晶之生長率。此時，薄膜磊晶生長率與厚度之均勻度則受反應器內反應物之流場性質及輸送現象(transport phenomena)，包含

• 氣流、速度、溫度分布、熱傳及質傳等因素之影響
• 氣體反應生成物之流速、進氣方式、基板
• 與反應室內之溫度、壓力及其幾何形狀等參數

對副產品之輸送現象也有極其重要之決定性

熱流耦合效應

• 反應器內因氣體之間溫差而有溫度梯度之存在，流場中之熱效應可藉由溫度特徵△T 表示之
• 熱浮力(thermal buoyancy effect)為反應器中最重要之熱效應，該熱效應之強弱可由一無因次之Grashof數表示之。

熱質傳耦合—擴散熱效應與熱擴散效應

• 在MOCVD 反應器中，由於溫度梯度與反應氣體各成分之濃度梯度共存，而有所謂熱質傳耦合現象。

以上摘錄至：郭峰鳴(93)，MOCVD 反應器之氮化鎵薄膜成長參數探討.

Aspect Extraction

Mukherjee and Liu (2012). Aspect Extraction through Semi-Supervised Modeling. ACL.

1. A key task of the framework is to extract aspects of entities that have been commented in opinion documents.
2. Two main types:
• The first type only extracts aspect terms without grouping them;
• The second type uses statistical topic models to extract aspects and group them.
3. This paper that given some seeds in the user interested categories.
4. The models are related to the DFLDA model in (Andrzejewski et al., 2009), while DF-LDA is only for topics/aspects.
5. There are many existing works on aspect extraction
• to find frequent noun terms and possibly with the help of dependency relations 
• to use supervised sequence labeling
6. Aspect and sentiment extraction using topic modeling come in two flavors:
• discovering aspect words sentiment wise (放在一起表示)
• separately discovering both aspects and sentiments (used Maximum-Entropy, Mei
  et al., 2007; Zhao et al., 2010)
• 思考上述兩種方法的優缺點，改進的空間
7. One problem with these existing models is that many discovered aspects are not understandable / meaningful to users.
8. Standard LDA and existing aspect and sentiment models based on document level, so many “non-specific” terms being pulled and clustered
9. Aspect terms tend to be nouns or noun phrases and sentiment terms tend to be adjectives, adverbs

Zhao et al., (2010). jointly modeling aspects and opinions with a mazEnt-LDA Hybrid. EMNLP.

1. Separateing aspects and opinion words can be very useful.
• can be used to construct a domain-dependent sentiment lexicon and applied to tasks such as sentiment classification. 
2. Global topic models may not be suitable for detecing rateable aspects.

Bagheri et al., (2013). An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews. NLDB.

1. Aspects are important because without knowing them, the opinions expressed in a sentence or a review are of limited use.

multi-aspect sentence

Many sentences in real reviews often involve two or more aspects.
The first sentence contains three single-aspect segments: an environment-segment (环境不错/ the environment is nice), a food-segment (菜品一般/ the quality of food is so so), and a charge-segment (很贵/ the food is very expensive)

Terminology

• topic: a multinomial distribution over words that represents a coherent concept in text.
• aspect: a multinomial distribution over words that represents a more speci c topic in reviews, for example,"lens" in camera reviews.
• senti-aspect: a multinomial distribution over words that represents a pair of aspect and sentiment, for example, "screen, positive" in a laptop review.
• affective word: a word that expresses a feeling, for example "satisfied", "disappointed".
• evaluative word: a word that expresses sentiment by evaluating an aspect, for example, "excellent", "nice".
• general evaluative word: an evaluative word that expresses a consistent sentiment every time it is used, for example, "good", "bad".
• aspect-specific evaluative word: an evaluative word that may express di erent sentiments depending on the aspect, for example, a "small" font size on a monitor that is hard to read vs. a "small" vacuum that is portable.
• sentiment word: a word that conveys sentiment. It is either an a ective word, general evaluative word, or aspect-speci c evaluative word.

source: Jo and Oh, WSDM'11.

Gold-standard lexicon

The gold-standard lexicon mentioned in the former case is obtained through one
of the following ways:
a) by manually tagging words from a domain corpus;
b) by one or more domain experts choosing aspects and keywords without the use of a
corpus; or
c) using review sets that have already been annotated with aspects and
keywords by the original reviewers