jmlr jmlr2012 jmlr2012-90 knowledge-graph by maker-knowledge-mining

90 jmlr-2012-Pattern for Python

Source: pdf

Author: Tom De Smedt, Walter Daelemans

Abstract: Pattern is a package for Python 2.4+ with functionality for web mining (Google + Twitter + Wikipedia, web spider, HTML DOM parser), natural language processing (tagger/chunker, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, k-means clustering, Naive Bayes + k-NN + SVM classiﬁers) and network analysis (graph centrality and visualization). It is well documented and bundled with 30+ examples and 350+ unit tests. The source code is licensed under BSD and available from http://www.clips.ua.ac.be/pages/pattern. Keywords: Python, data mining, natural language processing, machine learning, graph networks

Reference: text

Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 BE CLiPS Computational Linguistics Group University of Antwerp 2000 Antwerp, Belgium Editor: Cheng Soon Ong Abstract Pattern is a package for Python 2. [sent-7, score-0.066]

2 It is well documented and bundled with 30+ examples and 350+ unit tests. [sent-9, score-0.115]

3 The source code is licensed under BSD and available from http://www. [sent-10, score-0.142]

4 Keywords: Python, data mining, natural language processing, machine learning, graph networks 1. [sent-15, score-0.117]

5 Introduction The World Wide Web is an immense collection of linguistic information that has in the last decade gathered attention as a valuable resource for tasks such as machine translation, opinion mining and trend detection, that is, “Web as Corpus” (Kilgarriff and Grefenstette, 2003). [sent-16, score-0.048]

6 This use of the WWW poses a challenge since the Web is interspersed with code (HTML markup) and lacks metadata (language identiﬁcation, part-of-speech tags, semantic labels). [sent-17, score-0.121]

7 “Pattern” (BSD license) is a Python package for web mining, natural language processing, machine learning and network analysis, with a focus on ease-of-use. [sent-18, score-0.312]

8 It offers a mash-up of tools often used when harnessing the Web as a corpus, which usually requires several independent toolkits chained together in a practical application. [sent-19, score-0.183]

9 Several such toolkits with a user interface exist in the scientiﬁc community, for example ORANGE (Demˇar et al. [sent-20, score-0.086]

10 By contrast, PATTERN is more related to toolkits such as NLTK (Bird et al. [sent-23, score-0.086]

11 The package aims to be useful to both a scientiﬁc and a non-scientiﬁc audience. [sent-28, score-0.066]

12 We believe that PATTERN is valuable as a learning environment for students, as a rapid development framework for web developers, and in research projects with a short development cycle. [sent-32, score-0.24]

13 Text is mined from the web and searched by syntax and semantics. [sent-35, score-0.374]

14 Package Overview PATTERN is organized in separate modules that can be chained together, as shown in Figure 1. [sent-38, score-0.101]

15 web Tools for web data mining, using a download mechanism that supports caching, proxies, asynchronous requests and redirection. [sent-45, score-0.332]

16 A SearchEngine class provides a uniform API to multiple web services: Google, Bing, Yahoo! [sent-46, score-0.166]

17 , Twitter, Wikipedia, Flickr and news feeds using FEED PARSER (packages. [sent-47, score-0.04]

18 The module includes an HTML parser based on BEAUTIFUL SOUP (crummy. [sent-50, score-0.407]

19 com/software/beautifulsoup), a PDF parser based on PDF M INER (unixuser. [sent-51, score-0.259]

20 en Fast, regular expressions-based shallow parser for English (identiﬁes sentence constituents, e. [sent-54, score-0.472]

21 , nouns, verbs), using a ﬁnite state part-of-speech tagger (Brill, 1992) extended with a tokenizer, lemmatizer and chunker. [sent-56, score-0.075]

22 A parser with higher accuracy (MBSP) can be plugged in. [sent-58, score-0.259]

23 The module has a Sentence class for parse tree traversal, functions for singularization/pluralization (Conway, 1998), conjugation, modality and sentiment analysis. [sent-59, score-0.344]

24 It comes bundled with WORDNET 3 (Fellbaum, 1998) and PYWORDNET. [sent-60, score-0.115]

25 en for Dutch, using the BRILL - NL language model (Geertzen, 2010). [sent-63, score-0.08]

26 Contributors are encouraged to read the developer documentation on how to add support for other languages. [sent-64, score-0.143]

27 Documents are lemmatized bag-of-words that can be grouped in a sparse corpus to compute TF-IDF, distance metrics (cosine, Euclidean, Manhattan, Hamming) and dimension reduction (Latent Semantic Analysis). [sent-75, score-0.186]

28 The module includes a hierarchical and a k-means clustering algorithm, optimized with the kmeans++ initialization algorithm (Arthur and Vassilvitskii, 2007) and triangle inequality (Elkan, 2003). [sent-76, score-0.148]

29 A Naive Bayes, a k-NN, and a SVM classiﬁer using LIBSVM (Chang and Li, 2011) are included, with tools for feature selection (information gain) and K-fold cross validation. [sent-77, score-0.033]

30 graph Graph data structure using Node, Edge and Graph classes, useful (for example) for modeling semantic networks. [sent-79, score-0.091]

31 The module has algorithms for shortest path ﬁnding, subgraph partitioning, eigenvector centrality and betweenness centrality (Brandes, 2001). [sent-80, score-0.468]

32 The module has a force-based layout algorithm that positions nodes in 2D space. [sent-82, score-0.148]

33 Evaluation metrics including a code proﬁler, functions for accuracy, precision and recall, confusion matrix, inter-rater agreement (Fleiss’ kappa), string similarity (Levenshtein, Dice) and readability (Flesch). [sent-87, score-0.114]

34 Example Script As an example, we chain together four PATTERN modules to train a k-NN classiﬁer on adjectives mined from Twitter. [sent-91, score-0.224]

35 First, we mine 1,500 tweets with the hashtag #win or #fail (our classes), for example: “$20 tip off a sweet little old lady today #win”. [sent-92, score-0.075]

36 We parse the part-of-speech tags for each tweet, keeping adjectives. [sent-93, score-0.158]

37 We group the adjective vectors in a corpus and use it to train the classiﬁer. [sent-94, score-0.335]

38 vector import import import import Twitter Sentence, parse search Document, Corpus, KNN corpus = Corpus() for i in range(1,15): for tweet in Twitter(). [sent-103, score-0.729]

39 lower() s = Sentence(parse(s)) s = search('JJ', s) # JJ = adjective s = [match[0]. [sent-108, score-0.149]

40 append(Document(s, type=p)) classifier = KNN() for document in corpus: classifier. [sent-111, score-0.095]

41 classify('stupid') # yields 'FAIL'd Figure 2: Example source code for a k-NN classiﬁer trained on Twitter messages. [sent-114, score-0.142]

42 Case Study As a case study, we used PATTERN to create a Dutch sentiment lexicon (De Smedt and Daelemans, 2012). [sent-116, score-0.239]

43 We mined online Dutch book reviews and extracted the 1,000 most frequent adjectives. [sent-117, score-0.147]

44 These were manually annotated with positivity, negativity, and subjectivity scores. [sent-118, score-0.178]

45 , 2007) we extracted the most frequent nouns and the adjectives preceding those nouns. [sent-121, score-0.222]

46 This results in a vector space with approximately 5,750 adjective vectors with nouns as features. [sent-122, score-0.261]

47 For each annotated adjective we then computed k-NN and inherited its scores to neighbor adjectives. [sent-123, score-0.215]

48 Documentation PATTERN comes bundled with examples and unit tests. [sent-127, score-0.115]

49 The documentation contains a quick overview, installation instructions, and for each module a detailed page with the API reference, examples of use and a discussion of the scientiﬁc principles. [sent-128, score-0.234]

50 The documentation assumes no prior knowledge, except for a background in Python programming. [sent-129, score-0.086]

51 Source Code is written in pure Python, meaning that we sacriﬁce performance for development speed and readability (i. [sent-133, score-0.084]

52 The package runs on all platforms and has no dependencies, with the exception of NumPy when LSA is used. [sent-136, score-0.066]

53 The source code is released under a BSD license, so it can be incorporated into proprietary products or used in combination with other open source packages such as SCRAPY (web mining), NLTK (natural language processing), PYBRAIN and PYML (machine learning) and NETWORKX (network analysis). [sent-141, score-0.297]

54 , 2010), a robust, memory-based shallow parser built on the TIMBL machine learning software. [sent-143, score-0.355]

55 The API’s for the PATTERN parser and MBSP are identical. [sent-144, score-0.259]

56 Gephi: An open source software for exploring and manipulating networks. [sent-150, score-0.075]

57 Jeroen geertzen :: software & demos : Brill-nl, June 2010. [sent-184, score-0.075]

58 Introduction to the special issue on the web as corpus. [sent-191, score-0.166]

59 A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. [sent-197, score-0.112]

similar papers computed by tfidf model

tfidf for this paper:

wordName wordTfidf (topN-words)

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