Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more.
Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems. We are particularly interested in algorithms that scale well and can be run efficiently in a highly distributed environment.
Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data, and recently have incorporated neural net technology.
On the semantic side, we identify entities in free text, label them with types (such as person, location, or organization), cluster mentions of those entities within and across documents (coreference resolution), and resolve the entities to the Knowledge Graph.
To learn more about the research we’re presenting at ACL 2020, please visit our Google at ACL 2020 website.
Interested in learning more about how we hire? Diversity and inclusion at Google? Watch demos performed by Googlers around the world? Please visit our Google at ACL 2020 website.