11,653 research outputs found
Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing
We present a novel technique to remove spurious ambiguity from transition
systems for dependency parsing. Our technique chooses a canonical sequence of
transition operations (computation) for a given dependency tree. Our technique
can be applied to a large class of bottom-up transition systems, including for
instance Nivre (2004) and Attardi (2006)
Treebank-based acquisition of a Chinese lexical-functional grammar
Scaling wide-coverage, constraint-based grammars such as Lexical-Functional Grammars (LFG) (Kaplan and Bresnan, 1982; Bresnan, 2001) or Head-Driven Phrase Structure Grammars (HPSG) (Pollard and Sag, 1994) from fragments to naturally occurring unrestricted text is knowledge-intensive, time-consuming and (often prohibitively) expensive. A number of researchers have recently presented methods to automatically acquire wide-coverage, probabilistic constraint-based grammatical resources from treebanks (Cahill et al., 2002, Cahill et al., 2003; Cahill et al., 2004; Miyao et al., 2003; Miyao et al., 2004; Hockenmaier and Steedman, 2002; Hockenmaier, 2003), addressing the knowledge acquisition bottleneck in constraint-based grammar development. Research to date has concentrated on English and German. In this paper we report on an experiment to induce wide-coverage, probabilistic LFG grammatical and lexical resources for Chinese from the Penn Chinese Treebank (CTB) (Xue et al., 2002) based on an automatic f-structure annotation algorithm. Currently 96.751% of the CTB trees receive a single, covering and connected f-structure, 0.112% do not receive an f-structure due to feature clashes, while 3.137% are associated with multiple f-structure fragments. From the f-structure-annotated CTB we extract a total of 12975 lexical entries with 20 distinct subcategorisation frame types. Of these 3436 are verbal entries with a total of 11 different frame types. We extract a number of PCFG-based LFG approximations. Currently our best automatically induced grammars achieve an f-score of 81.57% against the trees in unseen articles 301-325; 86.06% f-score (all grammatical functions) and 73.98% (preds-only) against the dependencies derived from the f-structures automatically generated for the original trees in 301-325 and 82.79% (all grammatical functions) and 67.74% (preds-only) against the dependencies derived from the manually annotated gold-standard f-structures for 50 trees randomly selected from articles 301-325
On Multilingual Training of Neural Dependency Parsers
We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistically relevant concepts can be inferred from word
spellings. We analyze the representations of characters and words that are
learned by the network to establish which properties of languages were
accounted for. In particular we show that the parser has approximately learned
to associate Latin characters with their Cyrillic counterparts and that it can
group Polish and Russian words that have a similar grammatical function.
Finally, we evaluate the parser on selected languages from the Universal
Dependencies dataset and show that it is competitive with other recently
proposed state-of-the art methods, while having a simple structure.Comment: preprint accepted into the TSD201
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a
sequential LSTM, to extract word order features while neglecting other valuable
syntactic information such as dependency graph or constituent trees. In this
paper, we first propose to use the \textit{syntactic graph} to represent three
types of syntactic information, i.e., word order, dependency and constituency
features. We further employ a graph-to-sequence model to encode the syntactic
graph and decode a logical form. Experimental results on benchmark datasets
show that our model is comparable to the state-of-the-art on Jobs640, ATIS and
Geo880. Experimental results on adversarial examples demonstrate the robustness
of the model is also improved by encoding more syntactic information.Comment: EMNLP'1
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