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An information extraction toolkit for bridging text and CogNet



We provide a professional and
integrated IE toolkit. CogIE takes
raw text as input and extracts entities, relations, events and frames with high-performance models.


We build a bridge between raw
text and CogNet. CogIE aligns the extracted facts to CogNet and leverages different types of knowledge to enrich results.


We contribute not just
user-friendly APIs, but an
extensible programming framework. Our goal in designing CogIE is to provide a universal toolkit for all sorts of users.


We decouple NLP
experiments, forming three
consecutive parts of
Training - Evaluation - Prediction.
CogIE is a programming framework to support function customization and model construction. You can easily carry out experiments on your own models with only minor modification.
Our easy-to-use APIs and
implementation can acclerate your deployment in the real-world
applications. You are more likely to use the high-level functions provided by the toolkit directly without knowing too many low-level details.
In order to allow students to
focus on building the core code
without the cumbersome details of
PyTorch, CogIE is encapsulated on top
of PyTorch. Less error-prone and more time-saving by automating most of the training loop and tricky engineering.


Named Entity Recognition

Named entity recognition (NER) is the task of identifying named entities like person, location, organization, drug, time, clinical procedure, biological protein, etc. in text. NER systems are often used as the first step in question answering, information retrieval, co-reference resolution, topic modeling, etc. CogIE can not only recognize the common four entity types: locations, persons, organizations, and miscellaneous entities, but also supports the recognition of 54 entity types.

Entity Typing

Entity Typing is an important task in text analysis. Assigning one or more types to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). There are 87 fine-grained entity lables (e.g., /person, /person/artist, /person/artist/actor) in CogIE.

Entity Linking

Entity linking is an essential component of many information extraction and Natural Language Understanding (NLU) pipelines since it resolves the lexical ambiguity of entity mentions and determines their meanings. CogIE bridges raw data with lots of KBs, the most critical of which is CogNet. CogNet is a KB dedicated to integrating three types of knowledge:

  • linguistic knowledge, which schematically describes situations, objects, and events;
  • world knowledge, which provides explicit knowledge about specific instances;
  • commonsense knowledge, which describes implicit general facts.
Relation Extraction

Relation extraction aims at predicting semantic relations between pairs of entities. More specifically, after identifying entity mentions in text, the main goal of RE is to classify relations. There are 500 relation classes in CogIE.

Event Extraction

Events are classified as things that happen or occur, and usually involve entities as their properties. Event extraction need to identify events that are composed of an event trigger, an event type, and a set of arguments with different roles.

Frame-Semantic Parsing

Frame semantic parsing is the task of automatically extracting semantic structures in text following the framework of FrameNet. It consists of three separate subtasks:

  • target identification: the task of identifying all frame evoking words in a given sentence;
  • frame identification: the task of identifying all frames of pre-identified targets in a given sentence;
  • argument identification: the task of identifying all frame-specific frame.

CogIE currently supports to identify 749 frames and 816 FEs in FrameNet.