TuLiPA - Parsing Extensions of TAG with Range Concatenation Grammars

4 pages, oral presentationInternational audienceIn this paper we present a parsing framework for extensions of Tree Adjoining Grammars (TAG) called TuLiPA (Tuebingen Linguistic Parsing Architecture). In particular, besides TAG, the parser can process Tree-Tuple MCTAG with shared nodes (TT-MCTAG), a TAG-extension that has been proposed to deal with scrambling in free word order languages such as German. The central strategy of the parser is such that the incoming TT-MCTAG (or TAG) is transformed into an equivalent Range Concatenation Grammar (RCG) which, in turn, is then used for parsing. The RCG parser is an incremental Earley-style chart parser. In addition to the syntactic anlysis, TuLiPA computes also an underspecified semantic analysis for grammars that are equipped with semantic representations

Joint RNN-Based Greedy Parsing and Word Composition

This paper introduces a greedy parser based on neural networks, which leverages a new compositional sub-tree representation. The greedy parser and the compositional procedure are jointly trained, and tightly depends on each-other. The composition procedure outputs a vector representation which summarizes syntactically (parsing tags) and semantically (words) sub-trees. Composition and tagging is achieved over continuous (word or tag) representations, and recurrent neural networks. We reach F1 performance on par with well-known existing parsers, while having the advantage of speed, thanks to the greedy nature of the parser. We provide a fully functional implementation of the method described in this paper.Comment: Published as a conference paper at ICLR 201

Incremental parsing algorithms for speech-editing mathematics and computer code

The provision of speech control for editing plain language text has existed for a long time, but does not extend to structured content such as mathematics. The requirements of a user interface for a spoken mathematics editor are explored through the lens of an intuitive natural user interface (NUI) for speech control, the desired properties of which are based on a combination of existing literature on NUIs and intuitive user interfaces. An important aspect of an intuitive NUI is timely update of display of the content in response to editing actions. This is not feasible using batch parsing alone, and this issue will be more serious for larger documents such as computer program code. The solution is an incremental parser designed to work with operator precedence (OP) grammars. The contribution to knowledge provided by this thesis is to improve the efficiency in terms of processing time, of the OP incremental parsing algorithm developed by Heeman, and extend it to handle the distfix (mixfix) operators described by Attanayake to model brackets and mathematical functions. This is implemented successfully for the TalkMaths system and shows a greatly reduced response time compared with using batch scanning and parsing alone. The author is not aware of any other incremental OP parser that handles such operators. Furthermore, a proposal is made for modifications to the data structures produced by Attanayake's parser, along with appropriate adjustments to the incremental parser, that will in the future, facilitate application of OP grammar to program code or other structured content by changing the definition of its content language

An Alternative Conception of Tree-Adjoining Derivation

The precise formulation of derivation for tree-adjoining grammars has important ramifications for a wide variety of uses of the formalism, from syntactic analysis to semantic interpretation and statistical language modeling. We argue that the definition of tree-adjoining derivation must be reformulated in order to manifest the proper linguistic dependencies in derivations. The particular proposal is both precisely characterizable through a definition of TAG derivations as equivalence classes of ordered derivation trees, and computationally operational, by virtue of a compilation to linear indexed grammars together with an efficient algorithm for recognition and parsing according to the compiled grammar.Comment: 33 page

A declarative characterization of different types of multicomponent tree adjoining grammars

Multicomponent Tree Adjoining Grammars (MCTAGs) are a formalism that has been shown to be useful for many natural language applications. The definition of non-local MCTAG however is problematic since it refers to the process of the derivation itself: a simultaneity constraint must be respected concerning the way the members of the elementary tree sets are added. Looking only at the result of a derivation (i.e., the derived tree and the derivation tree), this simultaneity is no longer visible and therefore cannot be checked. I.e., this way of characterizing MCTAG does not allow to abstract away from the concrete order of derivation. In this paper, we propose an alternative definition of MCTAG that characterizes the trees in the tree language of an MCTAG via the properties of the derivation trees (in the underlying TAG) the MCTAG licences. We provide similar characterizations for various types of MCTAG. These characterizations give a better understanding of the formalisms, they allow a more systematic comparison of different types of MCTAG, and, furthermore, they can be exploited for parsing

Interleaving natural language parsing and generation through uniform processing

We present a new model of natural language processing in which natural language parsing and generation are strongly interleaved tasks. Interleaving of parsing and generation is important if we assume that natural language understanding and production are not only performed in isolation but also can work together to obtain subsentential interactions in text revision or dialog systems. The core of the model is a new uniform agenda-driven tabular algorithm, called UTA. Although uniformly defined, UTA is able to configure itself dynamically for either parsing or generation, because it is fully driven by the structure of the actual input - a string for parsing and a semantic expression for generation. Efficient interleaving of parsing and generation is obtained through item sharing between parsing and generation. This novel processing strategy facilitates exchanging items (i.e., partial results) computed in one direction automatically to the other direction as well. The advantage of UTA in combination with the item sharing method is that we are able to extend the use of memorization techniques even to the case of an interleaved approach. In order to demonstrate UTA\u27s utility for developing high-level performance methods, we present a new algorithm for incremental self-monitoring during natural language production

Interleaving natural language parsing and generation through uniform processing

We present a new model of natural language processing in which natural language parsing and generation are strongly interleaved tasks. Interleaving of parsing and generation is important if we assume that natural language understanding and production are not only performed in isolation but also can work together to obtain subsentential interactions in text revision or dialog systems. The core of the model is a new uniform agenda-driven tabular algorithm, called UTA. Although uniformly defined, UTA is able to configure itself dynamically for either parsing or generation, because it is fully driven by the structure of the actual input - a string for parsing and a semantic expression for generation. Efficient interleaving of parsing and generation is obtained through item sharing between parsing and generation. This novel processing strategy facilitates exchanging items (i.e., partial results) computed in one direction automatically to the other direction as well. The advantage of UTA in combination with the item sharing method is that we are able to extend the use of memorization techniques even to the case of an interleaved approach. In order to demonstrate UTA's utility for developing high-level performance methods, we present a new algorithm for incremental self-monitoring during natural language production

Parsing Inside-Out

The inside-outside probabilities are typically used for reestimating Probabilistic Context Free Grammars (PCFGs), just as the forward-backward probabilities are typically used for reestimating HMMs. I show several novel uses, including improving parser accuracy by matching parsing algorithms to evaluation criteria; speeding up DOP parsing by 500 times; and 30 times faster PCFG thresholding at a given accuracy level. I also give an elegant, state-of-the-art grammar formalism, which can be used to compute inside-outside probabilities; and a parser description formalism, which makes it easy to derive inside-outside formulas and many others.Comment: Ph.D. Thesis, 257 pages, 40 postscript figure

Neural Combinatory Constituency Parsing

東京都立大学Tokyo Metropolitan University博士（情報科学）doctoral thesi