Introduction to the CoNLL-2001 Shared Task: Clause Identification

We describe the CoNLL-2001 shared task: dividing text into clauses. We give background information on the data sets, present a general overview of the systems that have taken part in the shared task and briefly discuss their performance

Alignment-guided chunking

We introduce an adaptable monolingual chunking approach–Alignment-Guided Chunking (AGC)–which makes use of knowledge of word alignments acquired from bilingual corpora. Our approach is motivated by the observation that a sentence should be chunked differently depending the foreseen end-tasks. For example, given the different requirements of translation into (say) French and German, it is inappropriate to chunk up an English string in exactly the same way as preparation for translation into one or other of these languages. We test our chunking approach on two language pairs: French–English and German–English, where these two bilingual corpora share the same English sentences. Two chunkers trained on French–English (FE-Chunker) and German–English(DE-Chunker ) respectively are used to perform chunking on the same English sentences. We construct two test sets, each suitable for French– English and German–English respectively. The performance of the two chunkers is evaluated on the appropriate test set and with one reference translation only, we report Fscores of 32.63% for the FE-Chunker and 40.41% for the DE-Chunker

Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance

Viable Dependency Parsing as Sequence Labeling

We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.Comment: Camera-ready version to appear at NAACL 2019 (final peer-reviewed manuscript). 8 pages (incl. appendix