Florian Laws


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End-to-End Information Extraction without Token-Level Supervision
Rasmus Berg Palm | Dirk Hovy | Florian Laws | Ole Winther
Proceedings of the Workshop on Speech-Centric Natural Language Processing

Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. To make matters worse, token-level labels are usually not the desired output, but just an intermediary step. End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels. We propose an E2E model based on pointer networks, which can be trained directly on pairs of raw input and output text. We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT movie corpus and compare to neural baselines that do use token-level labels. We achieve competitive results, within a few percentage points of the baselines, showing the feasibility of E2E information extraction without the need for token-level labels. This opens up new possibilities, as for many tasks currently addressed by human extractors, raw input and output data are available, but not token-level labels.


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Active Learning for Coreference Resolution
Florian Laws | Florian Heimerl | Hinrich Schütze
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Active Learning with Amazon Mechanical Turk
Florian Laws | Christian Scheible | Hinrich Schütze
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing


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Building a Cross-lingual Relatedness Thesaurus using a Graph Similarity Measure
Lukas Michelbacher | Florian Laws | Beate Dorow | Ulrich Heid | Hinrich Schütze
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The Internet is an ever growing source of information stored in documents of different languages. Hence, cross-lingual resources are needed for more and more NLP applications. This paper presents (i) a graph-based method for creating one such resource and (ii) a resource created using the method, a cross-lingual relatedness thesaurus. Given a word in one language, the thesaurus suggests words in a second language that are semantically related. The method requires two monolingual corpora and a basic dictionary. Our general approach is to build two monolingual word graphs, with nodes representing words and edges representing linguistic relations between words. A bilingual dictionary containing basic vocabulary provides seed translations relating nodes from both graphs. We then use an inter-graph node-similarity algorithm to discover related words. Evaluation with three human judges revealed that 49% of the English and 57% of the German words discovered by our method are semantically related to the target words. We publish two resources in conjunction with this paper. First, noun coordinations extracted from the German and English Wikipedias. Second, the cross-lingual relatedness thesaurus which can be used in experiments involving interactive cross-lingual query expansion.

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A Linguistically Grounded Graph Model for Bilingual Lexicon Extraction
Florian Laws | Lukas Michelbacher | Beate Dorow | Christian Scheible | Ulrich Heid | Hinrich Schütze
Coling 2010: Posters

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Sentiment Translation through Multi-Edge Graphs
Christian Scheible | Florian Laws | Lukas Michelbacher | Hinrich Schütze
Coling 2010: Posters


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A Graph-Theoretic Algorithm for Automatic Extension of Translation Lexicons
Beate Dorow | Florian Laws | Lukas Michelbacher | Christian Scheible | Jason Utt
Proceedings of the Workshop on Geometrical Models of Natural Language Semantics

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On Proper Unit Selection in Active Learning: Co-Selection Effects for Named Entity Recognition
Katrin Tomanek | Florian Laws | Udo Hahn | Hinrich Schütze
Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing


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Stopping Criteria for Active Learning of Named Entity Recognition
Florian Laws | Hinrich Schütze
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Estimation of Conditional Probabilities With Decision Trees and an Application to Fine-Grained POS Tagging
Helmut Schmid | Florian Laws
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)