Experiments in multi-modal automatic content extraction

Ramshaw, Lance; Boschee, Elizabeth; Bratus, Sergey; Miller, S.L.; Stone, Rebecca; Weischedel, Ralph; Zamanian, Alex

doi:10.3115/1072133.1072176

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…Our cross-document co-reference system relies on SERIF, a state-of-the-art information extraction (IE) system (Ramshaw, et. al, 2001) for documentlevel information extraction.…”

Section: Extraction System Overviewmentioning

confidence: 99%

Who is who and what is what

Baron¹,

Freedman²

2008

Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08

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This paper describes a language-independent, scalable system for both challenges of crossdocument co-reference: name variation and entity disambiguation. We provide system results from the ACE 2008 evaluation in both English and Arabic. Our English system's accuracy is 8.4% relative better than an exact match baseline (and 14.2% relative better over entities mentioned in more than one document). Unlike previous evaluations, ACE 2008 evaluated both name variation and entity disambiguation over naturally occurring named mentions. An information extraction engine finds document entities in text. We describe how our architecture designed for the 10K document ACE task is scalable to an even larger corpus. Our cross-document approach uses the names of entities to find an initial set of document entities that could refer to the same real world entity and then uses an agglomerative clustering algorithm to disambiguate the potentially co-referent document entities. We analyze how different aspects of our system affect performance using ablation studies over the English evaluation set. In addition to evaluating cross-document coreference performance, we used the results of the cross-document system to improve the accuracy of within-document extraction, and measured the impact in the ACE 2008 withindocument evaluation.

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“…Our cross-document co-reference system relies on SERIF, a state-of-the-art information extraction (IE) system (Ramshaw, et. al, 2001) for documentlevel information extraction.…”

Section: Extraction System Overviewmentioning

confidence: 99%

Who is who and what is what

Baron¹,

Freedman²

2008

Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08

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“…A sentence is considered relevant if it contains the query keyword or contains a word that is co-referent to the query term. Coreference is determined using an information extraction engine, SERIF (Ramshaw et al, 2001). We then conduct deep linguistic analysis and pattern matching to extract candidate answers.…”

Section: A Hybrid Approach To Definitional Question Answeringmentioning

confidence: 99%

“…We use SERIF (Ramshaw et al, 2001), a linguistic analysis engine, to perform full parsing, name entity detection, relation detection, and co-reference resolution. We extract the following linguistic features:…”

Section: Deep Linguistic Analysismentioning

confidence: 99%

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Combining deep linguistics analysis and surface pattern learning

Peng¹,

Weischedel²,

Licuanan³

et al. 2005

Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05

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We explore a hybrid approach for Chinese definitional question answering by combining deep linguistic analysis with surface pattern learning. We answer four questions in this study: 1) How helpful are linguistic analysis and pattern learning? 2) What kind of questions can be answered by pattern matching? 3) How much annotation is required for a pattern-based system to achieve good performance? 4) What linguistic features are most useful? Extensive experiments are conducted on biographical questions and other definitional questions. Major findings include: 1) linguistic analysis and pattern learning are complementary; both are required to make a good definitional QA system; 2) pattern matching is very effective in answering biographical questions while less effective for other definitional questions; 3) only a small amount of annotation is required for a pattern learning system to achieve good performance on biographical questions; 4) the most useful linguistic features are copulas and appositives; relations also play an important role; only some propositions convey vital facts.

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“…Many systems use a greedy strategy [11], scanning through the text and choosing for each mention whether or not it links back, and if so, to which previous entity. Other researchers have explored graph-partitioning approaches [8] that consider the whole document at once.…”

Section: Approachesmentioning

confidence: 99%

Information Extraction

Ramshaw¹,

Weischedel²

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

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Information Extraction (IE) maps a language stream into database records that capture part of its meaning. Name, entity, and relation extraction are common subtasks, and an event extraction subtask has also been proposed. Various specific target sets have been defined, with results compared in government-sponsored evaluations, but defining 'meaning' for a broad spectrum of applications remains a challenge. Researchers are exploring a wide variety of learning techniques for these tasks. Initial tests have also been performed measuring IE performance on speech recognition output, but current work has only scratched the surface of this important area.

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Experiments in multi-modal automatic content extraction

Cited by 9 publications

References 2 publications

Who is who and what is what

Who is who and what is what

Combining deep linguistics analysis and surface pattern learning

Information Extraction

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