#OPEN SOURCE DATA EXTRACTOR MOVIE#
Another example is to identify sentiment expressed in a text, e.g., “This movie was awesome”. Here, Barack Obama and USA are named entities of types of PERSON and LOCATION, respectively. One example of IE is to identify named entities in a text, e.g., “Barack Obama served as the president of the USA”. The community of Information Retrieval (IR) relies on accurate and high-performance IE to be able to retrieve high quality results from massive datasets. Ībstract = "Information extraction (IE) is a common sub-area of natural language processing that focuses on identifying structured data from unstructured data. The tools introduced in the tutorial will focus on the three main stages of IE, namely, collection of data (including annotation), data processing and analytics, and visualization of the extracted information. Participants will also learn how social context can be integrated in Information Extraction systems to make them better. Finally, participants will be trained to use a suite of open source tools (SAIL for active learning, TwitterNER for named entity recognition3, and SocialMediaIE for multi task learning), which utilize advanced machine learning techniques (e.g., deep learning, active learning with human-in-the-loop, multi-lingual, and multi-task learning) to perform IE on their own or existing datasets. Additionally, participants will be familiarized with the landscape of publicly available tweet data, and methods for collecting and preparing them for analysis. Participants will learn and practice various semantic and syntactic IE techniques that are commonly used for analyzing tweets. This tutorial introduces participants to a) the usage of Python based, open-source tools that support IE from social media data (mainly Twitter), and b) best practices for ensuring the reproducibility of research. Finally, identifying various linguistic aspects of a text, e.g., part of speech tags, noun phrases, dependency parses, etc., which can serve as features for additional IE tasks.
Here, the sentiment expressed is positive.
Information extraction (IE) is a common sub-area of natural language processing that focuses on identifying structured data from unstructured data.
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