SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments

Named entity recognition and linking in tweets based on linguistic similarity

This work proposes a novel approach in Named Entity rEcognition and Linking (NEEL) in tweets, applying the same strategy already presented for Question Answering (QA) by the same authors. The previous work describes a rule-based and ontology-based system that attempts to retrieve the correct answer to a query from the DBPedia ontology through a similarity measure between the query and the ontology labels. In this paper, a tweet is interpreted as a query for the QA system: both the text and the thread of a tweet are a sequence of statements that have been linked to the ontology. Provided that tweets make extensive use of informal language, the similarity measure and the underlying processes have been devised differently than in the previous approach; also the particular structure of a tweet, that is the presence of mentions, hashtags, and partially structured statements, is taken into consideration for linguistic insights. NEEL is achieved actually as the output of annotating a tweet with the names of the ontological entities retrieved by the system. The strategy is explained in detail along with the architecture and the implementation of the system; also the performance as compared to the systems presented at the #Micropost2016 workshop NEEL Challenge co-located with the World Wide Web conference 2016 (WWW â16) is reported and discussed

Named Entity Recognition for Chinese Novels in the Ming-Qing Dynasties

Chinese Lexical Semantics 17th Workshop, CLSW 2016, Singapore, Singapore, May 20–22, 2016This paper presents a Named Entity Recognition (NER) system for Chinese classic novels in the Ming and Qing dynasties using the Conditional Random Fields (CRFs) method. An annotated corpus of four influential vernacular novels produced during this period is used as both training and testing data. In the experiment, three novels are used as training data and one novel is used as the testing data. Three sets of features are proposed for the CRFs model: (1) baseline feature set, that is, word/POS and bigram for different window sizes, (2) dependency head and dependency relationship, and (3) Wikipedia categories. The F-measures for these four books range from 67% to 80%. Experiments show that using the dependency head and relationship as well as Wikipedia categories can improve the performance of the NER system. Compared with the second feature set, the third one can produce greater improvement.Department of ComputingDepartment of Chinese and Bilingual Studies2016-2017 > Academic research: refereed > Chapter in an edited book (author)bcw