mp-procs 0.1.1

Common lightweight PyTerrier adapters for specific artifact shapes - query and document processors for modular IR pipelines

MP-Procs: Modular Pipeline Query and Document Processors

A PyTerrier-compatible package providing modular query and document processing components for information retrieval pipelines.

Overview

MP-Procs offers a collection of transformers that can be seamlessly integrated into PyTerrier pipelines to enhance query processing and document indexing. The package is designed for experimental IR research, allowing researchers to easily combine and evaluate different processing strategies.

Features

Query Processors (mp_procs.qproc)

• Segmentation-based Query Enhancement

  • weighted_segmentation_boost: Boost terms in multi-word segments
  • append_segmentation_with_or: Append segments using logical OR with #syn/#band operators
  • synonym_segmentation: Treat segments as synonym groups with curly braces

• Query Intelligence

  • intent_trigger_weighted: Add intent-specific trigger phrases based on predicted query intent
  • single_rare_term_emphasis_weighted: Emphasize rare terms based on IDF statistics

• Text Preprocessing

  • sanitize_column_transform: Remove special characters and clean query text

Document Processors (mp_procs.dproc)

• Query Generation

  • append_query_gen: Generate additional queries using DocT5Query

• Content Enhancement

  • process_keyphrases: Extract and append keyphrases to document content

Installation

# Install from local source
cd /path/to/thesis_code/mp_procs
pip install -e .

Requirements

• python-terrier >= 0.10.0
• pyterrier-alpha
• pandas

Pipeline Integration

Complete IR Pipeline Example

import pyterrier as pt
import pyterrier-alpha as pta
from mp_procs.qproc import weighted_segmentation_boost, sanitize_column_transform
from mp_procs.dproc import process_keyphrases

# Load dataset
dataset = pt.get_dataset("irds:trec-robust04")
topics, qrels = dataset.get_topics("title"), dataset.get_qrels()

# Query processing
query_artifact = pta.Artifact.from_url("tira:disks45/nocr/trec-robust-2004/ows/query-segmentation-hyb-a")
query_enhancer = weighted_segmentation_boost(boost_weight=1.2)
query_pipeline = query_artifact >> query_enhancer
topics = query_pipeline(topics)

# Document processing and indexing
keyphrase_proc = process_keyphrases(artifact=keyphrase_artifact, repeat=2)
indexer = pt.IterDictIndexer("./enhanced_index", overwrite=True, meta={"docno": 20})
index_ref = (keyphrase_proc >> indexer).index(dataset.get_corpus_iter())

# Retrieval
retriever = pt.terrier.Retriever(index_ref, wmodel="BM25")

# Complete pipeline
pipeline = query_pipeline >> retriever

# Evaluate
results = pipeline.transform(topics)
evaluation = pt.Evaluate(results, qrels, metrics=["map", "ndcg_cut_10"])
print(evaluation)

Configuration Options

Query Processors

Function	Key Parameters	Description
weighted_segmentation_boost	boost_weight=1.2, seg_col="segmentation"	Boost factor for segment terms
append_segmentation_with_or	seg_col="segmentation"	Use #syn/#band operators
synonym_segmentation	seg_col="segmentation"	Create {synonym groups}
intent_trigger_weighted	trigger_weight=1.5, intent_col="intent_prediction"	Weight for trigger phrases
single_rare_term_emphasis_weighted	emphasis_weight=1.5, avg_idf_low=5.0	Rare term boosting thresholds
sanitize_column_transform	source_col="query", target_col="query"	Column mapping for cleaning

Document Processors

Function	Key Parameters	Description
append_query_gen	repeat=1	Number of generated queries to append
process_keyphrases	repeat=1	Number of keyphrase extractions

Entry Points

The package registers entry points for automatic discovery:

[project.entry-points."modpipe.qproc"]
SegmentationWeighted = "mp_procs.qproc:weighted_segmentation_boost"
SegmentationAppendOr = "mp_procs.qproc:append_segmentation_with_or"
SegmentationSynonyms = "mp_procs.qproc:synonym_segmentation"
PredictorBoost = "mp_procs.qproc:single_rare_term_emphasis_weighted"
IntentTriggerAppend = "mp_procs.qproc:intent_trigger_weighted"
SpellingModification = "mp_procs.qproc:sanitize_column_transform"

[project.entry-points."modpipe.dproc"]
DocT5Query = "mp_procs.dproc:append_query_gen"
KeyphraseExtraction = "mp_procs.dproc:process_keyphrases"

Testing

Run the test suite:

cd mp_procs
python -m pytest tests/ -v

Or run specific tests:

python -m unittest tests.test_queryprocessor.TestQueryProcessors.test_weighted_segmentation_boost_basic

Error Handling

All processors gracefully handle missing columns and invalid data:

• Missing required columns → return DataFrame unchanged
• Empty/null values → skip processing for those rows
• Invalid data types → attempt conversion or skip gracefully
• Chain compatibility → all processors accept and return pandas DataFrames

Contributing

This package is part of a research thesis on modular IR pipelines. For issues or contributions:

1. Follow PyTerrier transformer conventions
2. Add comprehensive tests for new processors
3. Update entry points in pyproject.toml
4. Maintain backward compatibility

License

This project is part of academic research. Please cite appropriately if used in publications.

Citation

If you use this package in your research, please cite:

@misc{mp_procs2025,
  title={MP-Procs: Modular Pipeline Processors for Information Retrieval},
  author={[Patrick Stahl]},
  year={2025},
  note={Thesis code for modular IR pipeline research}
}