1. Overview of StreamCorpus software components

StreamCorpus provides a toolkit for processing massive streams of text and running natural language extractors on the text. StreamCorpus does not provide any extractors itself; it operates third-party extractors, such as Serif and Factorie, and unifies their output. StreamCorpus pipline can store documents in S3, Accumulo, or flat files.

StreamCorpus is used extensively in TREC KBA, TREC Temporal Summarization, TREC Dynamic Domain, and at Diffeo.

Ask more at StreamCorpus Google Group

All of these python package are hosted in github.com/trec-kba and github.com/diffeo

1.1. Invocation

The general data flow is as follows:

  1. Convert your input format into streamcorpus.StreamItem format. This generally needs to be done by custom code implementing the streamcorpus_pipeline reader stage interface.
  2. Run streamcorpus_pipeline over the original inputs to produce streamcorpus.Chunk files, either stored locally or in kvlayer backed storage.

All of the programs share a common configuration interface. You can pass --dump-config to any of the programs to see the default configuration, and --config to any of them to provide your own configuration file.

1.2. Example

One convenient path to load data is to use the yaml_files_list reader to load in plain-text data files matching known entities. We will load the data into Apache Accumulo as a backing database. Create a shared configuration file, common.yaml, that includes the basic shared setup, as well as some basic logging configuration and support for the rejester distributed computing environment:

     level: INFO
   app_name: datasets
   namespace: mydataset
   storage_type: accumulo
   storage_addresses: [ "accumulo-proxy.example.com:50096" ]
   username: root
   password: secret
rejester: # necessary but unused in this example
  app_name: datasets
  namespace: mydataset
  registry_addresses: [ "redis.example.com:6379" ]

The reader needs a specific YAML file to tell it where to find input documents and how to label them. This file, labels.yaml, looks like:

root_path:                # "empty" means working directory
source: source            # embedded in StreamItem.source
annotator_id: annotator   # embedded in labels
  - target_id: https://kb.diffeo.com/entity
    doc_path: data
      - canonical_name: Entity
      - entity

This will cause the reader to read the documents under the data path, create a stream item for each marked as coming from source, and search each for appearances of the term “entity”. Mentions of that term will be labelled as corresponding to the https://kb.diffeo.com/entity entity, according to the annotator “annotator”.

A streamcorpus_pipeline configuration that reads this using the Serif NLP tagger can be stored in streamcorpus_pipeline.yaml:

# ... paste common.yaml here ...

  third_dir_path: /third
  tmp_dir_path: tmp
  output_chunk_max_count: 500
  reader: yaml_files_list
    - language
    - guess_media_type
    - clean_html
    - hyperlink_labels
    - clean_visible
  batch_transforms: [ serif ]
  writers: [ to_kvlayer ]
    require_abs_url: true
    all_domains: true
    offset_types: [ BYTES, CHARS ]
    path_in_third: serif/serif-latest
    cleanup_tmp_files: true
    par: streamcorpus_one_step
    align_labels_by: names_in_chains
      chain_selector: ANY_MULTI_TOKEN
      annotator_id: annotator

Then you can run

streamcorpus_pipeline \
  --config streamcorpus_pipeline.yaml --input labels.yaml

1.3. Module dependencies

digraph modules {
streamcorpus_pipeline -> streamcorpus
streamcorpus_pipeline -> yakonfig
streamcorpus_pipeline -> kvlayer [style=dotted]
streamcorpus_pipeline -> dblogger
streamcorpus_pipeline -> rejester [style=dotted]
rejester -> yakonfig
kvlayer -> yakonfig