24 research outputs found
Data-Oriented Parsing with discontinuous constituents and function tags
Statistical parsers are e ective but are typically limited to producing projective dependencies or constituents. On the other hand, linguisti- cally rich parsers recognize non-local relations and analyze both form and function phenomena but rely on extensive manual grammar development. We combine advantages of the two by building a statistical parser that produces richer analyses.
We investigate new techniques to implement treebank-based parsers that allow for discontinuous constituents. We present two systems. One system is based on a string-rewriting Linear Context-Free Rewriting System (LCFRS), while using a Probabilistic Discontinuous Tree Substitution Grammar (PDTSG) to improve disambiguation performance. Another system encodes the discontinuities in the labels of phrase structure trees, allowing for efficient context-free grammar parsing.
The two systems demonstrate that tree fragments as used in tree-substitution grammar improve disambiguation performance while capturing non-local relations on an as-needed basis. Additionally, we present results of models that produce function tags, resulting in a more linguistically adequate model of the data. We report substantial accuracy improvements in discontinuous parsing for German, English, and Dutch, including results on spoken Dutch
Data-Oriented Parsing with Discontinuous Constituents and Function Tags
Statistical parsers are e ective but are typically limited to producing projective dependencies or constituents. On the other hand, linguisti- cally rich parsers recognize non-local relations and analyze both form and function phenomena but rely on extensive manual grammar development. We combine advantages of the two by building a statistical parser that produces richer analyses. We investigate new techniques to implement treebank-based parsers that allow for discontinuous constituents. We present two systems. One system is based on a string-rewriting Linear Context-Free Rewriting System (LCFRS), while using a Probabilistic Discontinuous Tree Substitution Grammar (PDTSG) to improve disambiguation performance. Another system encodes the discontinuities in the labels of phrase structure trees, allowing for efficient context-free grammar parsing. The two systems demonstrate that tree fragments as used in tree-substitution grammar improve disambiguation performance while capturing non-local relations on an as-needed basis. Additionally, we present results of models that produce function tags, resulting in a more linguistically adequate model of the data. We report substantial accuracy improvements in discontinuous parsing for German, English, and Dutch, including results on spoken Dutch
Discontinuous Data-Oriented Parsing: A mildly context-sensitive all-fragments grammar
Recent advances in parsing technology have made treebank parsing with discontinuous constituents possible, with parser output of competitive quality (Kallmeyer and Maier, 2010). We apply Data-Oriented Parsing (DOP) to a grammar formalism that allows for discontinuous trees (LCFRS). Decisions during parsing are conditioned on all possible fragments, resulting in improved performance. Despite the fact that both DOP and discontinuity present formidable challenges in terms of computational complexity, the model is reasonably efficient, and surpasses the state of the art in discontinuous parsing.
Parsing as Reduction
We reduce phrase-representation parsing to dependency parsing. Our reduction
is grounded on a new intermediate representation, "head-ordered dependency
trees", shown to be isomorphic to constituent trees. By encoding order
information in the dependency labels, we show that any off-the-shelf, trainable
dependency parser can be used to produce constituents. When this parser is
non-projective, we can perform discontinuous parsing in a very natural manner.
Despite the simplicity of our approach, experiments show that the resulting
parsers are on par with strong baselines, such as the Berkeley parser for
English and the best single system in the SPMRL-2014 shared task. Results are
particularly striking for discontinuous parsing of German, where we surpass the
current state of the art by a wide margin