Our Natural Language Processing API contains all the necessary text processing tools one might expect from an NLP API, including tokenization, sentence splitting, part-of-speech tagging and named entity recognition.
Around the web
Courier's Task Extraction - Rodrigo Alarcón - MediumIn a previous post we detailed some general issues regarding labelling data for training a supervised Machine Learning algorithm to identify task sentences in emails. As we mentioned, there were many borderline cases such as ambiguity, conditionality or general email pleasantries that made the labelling of task and non-task sentences a very complex challenge.
Summarizing with Grammar and Discourse Rules - David Schueler - MediumIn the previous blog post, we introduced the workflow of Courier's summarizer of conversational emails, from the machine-learning component to the post-processing component. In this post, we look at the latter, post-processing component in more depth. Note that the examples given here are made up; they are not from actual emails, but serve to show the canonical case for the role of each rule.
Learning Emotions from Reddit - techburstEveryday, our inboxes are flooded with a ton of computer-generated messages, what we internally call 'botmails', letting us know about updates for services we use, purchases we have made, promotions trying to convince us to buy the latest and shiniest products, etc.
Sarcasm Detection: A First Approach - Paulo Malvar - MediumAs discussed on a previous post, capturing pragmatic phenomena, that is, phenomena that go beyond the realms of morphology, syntax and distributional lexical and compositional semantics, is crucial for the success of natural language understanding (NLU) projects. On this post we would like to discuss our effort in training a Machine Learning classifier that is able to detect sarcasm in textual conversations.