Get the free Supervised Within-Document Event Coreference using ... - Jun Araki - lrec-conf

Supervised Within-Document Event Coreference using Information Propagation Zhengzhou Liu, Jun Arab, Eduard Holy, Peruke McNamara Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA Liu×cs.CMU.edu,

How to fill out supervised within-document event coreference

How to fill out supervised within-document event coreference:

1. Start by understanding the concept of within-document event coreference. Within-document event coreference refers to the task of identifying and linking various event mentions within a single document that refer to the same real-world event. It involves determining which event mentions are coreferent and establishing their relationships.
2. Familiarize yourself with the available annotated data or resources for supervised within-document event coreference. These resources typically consist of annotated documents where event mentions and their coreferent links are marked. It is essential to understand the annotation guidelines and any specific criteria used for identifying event coreference.
3. Select a suitable supervised machine learning algorithm or model for within-document event coreference. Various models can be used for this task, including decision trees, support vector machines (SVMs), or more advanced deep learning models like neural networks. Choose an algorithm that has been shown to perform well on event coreference tasks.
4. Prepare your training data by extracting event mentions and their coreferent links from annotated documents. This step involves identifying event mentions and their corresponding attributes, such as type, time, location, participants, etc. Create a training dataset consisting of event mention pairs and their labels indicating whether they are coreferent or not.
5. Design and extract relevant features for event coreference resolution. Features can include syntactic information, semantic features, lexical and contextual details, and any other attributes that can help identify coreference relationships. Feature engineering is a crucial step in improving the performance of event coreference models.
6. Train your supervised model using the prepared training data and the selected machine learning algorithm. The model will learn to predict whether a pair of event mentions are coreferent based on the extracted features. Evaluate the performance of your trained model using appropriate metrics like precision, recall, and F1 score.

Who needs supervised within-document event coreference:

1. Researchers and practitioners working in the field of natural language processing (NLP) and information extraction. Within-document event coreference is an important task in NLP, with applications in various domains like information retrieval, text summarization, question answering, and more. Researchers and practitioners in these areas need supervised within-document event coreference to improve the accuracy and effectiveness of their systems.
2. Developers and engineers working on data-driven applications that rely on event-related information. For applications like event monitoring, news aggregation, or social media analysis, understanding the relationships between event mentions within a document can be crucial. Supervised within-document event coreference can help enhance the performance and precision of event-based applications.
3. Language service providers and data annotation companies. These entities often provide annotation services to enhance datasets for training NLP models. Supervised within-document event coreference is a specific annotation task that may be required by clients looking to develop event-related applications or improve their existing NLP capabilities.

For pdfFiller’s FAQs

What is supervised within-document event coreference?

Supervised within-document event coreference is a task in natural language processing that aims to link together different mentions of the same event within a single document.

Who is required to file supervised within-document event coreference?

Researchers, developers, and practitioners in the field of natural language processing are typically the ones who work on supervised within-document event coreference.

How to fill out supervised within-document event coreference?

To fill out supervised within-document event coreference, one would typically use annotated datasets and machine learning algorithms to train a model to identify and link event mentions.

What is the purpose of supervised within-document event coreference?

The purpose of supervised within-document event coreference is to improve event extraction and event understanding in natural language processing tasks.

What information must be reported on supervised within-document event coreference?

Information such as the event mentions, their attributes, and the links between them must be reported on supervised within-document event coreference.

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