Name
Abstract | ||
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We observe that most relevant terms in unstructured news articles are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of news articles. Our experimentation, conducted using three publicly available news datasets, demonstrates that BM25P markedly outperforms BM25 in term of effectiveness by up to 17.44% in [email protected] and 85% in [email protected]
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3331184.3331373 | Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | Field | DocType |
bm25, news retrieval, weighting model | Linear combination,Weighting,Information retrieval,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-4503-6172-9 | 1 | 0.38 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matteo Catena | 1 | 14 | 3.58 |
| Ophir Frieder | 2 | 3300 | 419.55 |
| Cristina Ioana Muntean | 3 | 32 | 8.28 |
| Franco Maria Nardini | 4 | 314 | 36.52 |
| Raffaele Perego | 5 | 1471 | 108.91 |
| Nicola Tonellotto | 6 | 377 | 39.90 |