MaltParser: A Data-Driven Parser-Generator for Dependency Parsing

By Joakim Nivre, Johan Hall, Jens Nilsson. 2006

618 citations recorded by the source corpus.

We introduce MaltParser, a data-driven parser generator for dependency parsing. Given a treebank in dependency format, MaltParser can be used to induce a parser for the language of the treebank. MaltParser supports several parsing algorithms and learning algorithms, and allows user-defined feature models, consisting of arbitrary combinations of lexical features, part-of-speech features and dependency features. MaltParser is freely available for research and educational purposes and has been evaluated empirically on Swedish, English, Czech, Danish and Bulgarian.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Model Architectures

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Citations in the corpus

From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions (2014, methodology): “Although we use the Malt parser (Nivre et al., 2006) to identify subject-verb-object dependencies, we have found it more accurate to develop deterministic heuristics and lexi-69 cal rules to identify the boundaries of complex (e.g. conjoined) NPs, allowing us to treat “a man with red shoes and a…”
Illinois-LH: A Denotational and Distributional Approach to Semantics (2014, methodology): “We lemmatize all sentences with the Stanford CoreNLP system 1 and extract syntactic chunks with the Illinois Chunker (Punyakanok and Roth, 2001).”
A Fast and Accurate Dependency Parser using Neural Networks (2014, methodology): “7 Furthermore, we also compare our parser with two popular, off-the-shelf parsers: Malt-Parser — a greedy transition-based dependency parser (Nivre et al., 2006), 8 and MSTParser — a ﬁrst-order graph-based parser (McDonald and Pereira, 2006).”
Fast and Accurate Shift-Reduce Constituent Parsing (2013, background): “Greedy, classiﬁer-based parsers have been developed for both dependency grammars (Yamada and Matsumoto, 2003; Nivre et al., 2006) and phrase-structure grammars (Sagae and Lavie, 2005).”
Semantic Parsing to Probabilistic Programs for Situated Question Answering (2016, methodology): “P 3 is designed to rely on approximate inference: our goal is to use rich features to accurately make lo-cal decisions, as in linear-time parsers (Nivre et al., 2006).”
Weakly Supervised Training of Semantic Parsers (2012, methodology): “We constructed the sentence corpus by ﬁrst sampling sentences from a web crawl and parsing them with MaltParser (Nivre et al., 2006).”
Extended Constituent-to-Dependency Conversion for English (2007, methodology): “We then ran MALTPARSER using the statistical models obtained from both dependency treebanks.”
A Tensor-based Factorization Model of Semantic Compositionality (2013, positive): “A parsed version of the corpus is available, which has been parsed with MaltParser (Nivre et al., 2006).”
A survey on question answering technology from an information retrieval perspective (2011, background): “Recently, state-of-the-art dependency parsers have been used to automatically represent queries as triplets and to disambiguate simple syntactic roles such as Subject, Object and indirect Object [96,66].”
An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing (2010, background): “, MALTPARSER (Nivre et al., 2006)), globally optimized graph-based parsers (e.”
News events prediction using Markov logic networks (2018, methodology): “We then used the Malt parser [51] to generate a dependency parser model for Persian language.”
Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation (2015, methodology): “For English→Russian, we copy the language-independent settings from the the English→German set-up, and perform dependency parsing with a Russian model for the Maltparser (Nivre et al., 2006; Sharoff and Nivre, 2011), applying projectivization after parsing.”
Multilingual Question Answering over Linked Data (QALD-3): Lab Overview (2013, neutral): “Resources CASIA Intui2 SWIP RTV WordNet [4] + + - Wikipedia - + - + PATTY(8) [12] + - - Tools CASIA Intui2 SWIP RTV WS4J (WordNet Similarity for Java)(9) - + - Chaos parser [2] - - - + MaltParser [13] - - + Stanford CoreNLP [9] + + - Jena ARQ query engine - + - Lucene(10) - - - + LARQ (Lucene + ARQ)”
splitSVM: Fast, Space-Efficient, non-Heuristic, Polynomial Kernel Computation for NLP Applications (2008, methodology): “On a sample application (replacing the libsvm classifier used by MaltParser (Nivre et al., 2006) with our own), we observe a speedup factor of 30 in parsing time.”
A Bayesian Model for Generative Transition-based Dependency Parsing (2015, methodology): “The UAS of our model is very close to that of the MaltParser.”
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling (2015, background): “The fact that Hindi and Arbic suffer the most from this issue is consistent with the poorer performance of their dependency parsers compared to other languages (Nivre et al., 2006; Green and Manning, 2010).”
Transition-based Dependency Parsing with Rich Non-local Features (2011, methodology): “Compared to graph-based dependency parsing, it typically offers linear time complexity and the comparative freedom to define non-local features, as exemplified by the comparison between MaltParser and MSTParser (Nivre et al., 2006b; McDonald et al., 2005; McDonald and Nivre, 2007).”