Alexis Nasr

Pseudo-Projectivity, A Polynomially Parsable Non-Projective Dependency Grammar

Dependency grammar has a long tradition in syntactic theory, dating back to at least Tesni~res work from the thirties3 Recently, it has gained renewed attention as empirical methods in parsing are discovering the importance of relations between words (see, e.g., (Collins, 1997)), which is what dependency grammars model explicitly do, but context-free phrasestructure grammars do not. One problem that has posed an impediment to more wide-spread acceptance of dependency grammars is the fact that there is no computationally tractable version of dependency grammar which is not restricted to projective analyses. However, it is well known that there are some syntactic phenomena (such as wh-movement in English or clitic climbing in Romance) that require nonprojective analyses. In this paper, we present a form of projectivity which we call pseudoprojectivity, and we present a generative stringrewriting formalism that can generate pseudoprojective analyses and which is polynomially parsable. The paper is structured as follows. In Section 2, we introduce our notion of pseudoprojectivity. We briefly review a previously proposed formalization of projective dependency grammars in Section 3. In Section 4, we extend this formalism to handle pseudo-projectivity. We informally present a parser in Section 5.

Tagging unknown proper names using decision trees

This paper describes a supervised learning method to automatically select from a set of noun phrases, embedding proper names of different semantic classes, their most distinctive features. The result of the learning process is a decision tree which classifies an unknown proper name on the basis of its context of occurrence. This classifier is used to estimate the probability distribution of an out of vocabulary proper name over a tagset. This probability distribution is itself used to estimate the parameters of a stochastic part of speech tagger.

MICA: A Probabilistic Dependency Parser Based on Tree Insertion Grammars (Application Note)

MICA is a dependency parser which returns deep dependency representations, is fast, has state-of-the-art performance, and is freely available.

Rapid Prototyping of Domain-Specific Machine Translation Systems

This paper reports on an experiment in assembling a domain-specific machine translation prototype system from off-the-shelf components. The design goals of this experiment were to reuse existing components, to use machine-learning techniques for parser specialization and for transfer lexicon extraction, and to use an expressive, lexicalized formalism for the transfer component.

Creating a finite-state parser with application semantics

Parsli is a finite-state (FS) parser which can be tailored to the lexicon, syntax, and semantics of a particular application using a hand-editable declarative lexicon. The lexicon is defined in terms of a lexicalized Tree Adjoining Grammar, which is subsequently mapped to a FS representation. This approach gives the application designer better and easier control over the natural language understanding component than using an off-the-shelf parser. We present results using Parsli on an application that creates 3D-images from typed input.

Joint Dependency Parsing and Multiword Expression Tokenization

Complex conjunctions and determiners are often considered as pretokenized units in parsing. This is not always realistic, since they can be ambiguous. We propose a model for joint dependency parsing and multiword expressions identification, in which complex function words are represented as individual tokens linked with morphological dependencies. Our graph-based parser includes standard second-order features and verbal subcategoriza-tion features derived from a syntactic lexicon .We train it on a modified version of the French Treebank enriched with morphological dependencies. It recognizes 81.79% of ADV+que conjunctions with 91.57% precision, and 82.74% of de+DET determiners with 86.70% precision.

Parsing with Lexicalized Probabilistic Recursive Transition Networks

We present a formalization of lexicalized Recursive Transition Networks which we call Automaton-Based Generative Dependency Grammar (gdg). We show how to extract a gdg from a syntactically annotated corpus, present a chart parser for gdg, and discuss different probabilistic models which are directly implemented in the finite automata and do not affect the parser.

Automatically building a Tunisian Lexicon for Deverbal Nouns

The sociolinguistic situation in Arabic countries is characterized by diglossia (Ferguson, 1959) : whereas one variant Modern Standard Arabic (MSA) is highly codified and mainly used for written communication, other variants coexist in regular everyday’s situations (dialects). Similarly, while a number of resources and tools exist for MSA (lexica, annotated corpora, taggers, parsers . . . ), very few are available for the development of dialectal Natural Language Processing tools. Taking advantage of the closeness of MSA and its dialects, one way to solve the problem of the lack of resources for dialects consists in exploiting available MSA resources and NLP tools in order to adapt them to process dialects. This paper adopts this general framework: we propose a method to build a lexicon of deverbal nouns for Tunisian (TUN) using MSA tools and resources as starting material.

Retrieving the syntactic structure of erroneous ASR transcriptions for open-domain Spoken Language Understanding

Retrieving the syntactic structure of erroneous ASR transcriptions can be of great interest for open-domain Spoken Language Understanding tasks in order to correct or at least reduce the impact of ASR errors on final applications. Most of the previous works on ASR and syntactic parsing have addressed this problem by using syntactic features during ASR to help reducing Word Error Rate (WER). The improvement obtained is often rather small, however the structure and the relations between words obtained through parsing can be of great interest for the SLU processes, even without a significant decrease of WER. That is why we adopt another point of view in this paper: considering that ASR transcriptions contain inevitably some errors, we show in this study that it is possible to improve the syntactic analysis of these erroneous transcriptions by performing a joint error detection / syntactic parsing process. The applicative framework used in this study is a speech-to-speech system developed through the DARPA BOLT project.

Constructing parse forests that include exactly the n-best PCFG trees

This paper describes and compares two algorithms that take as input a shared PCFG parse forest and produce shared forests that contain exactly the n most likely trees of the initial forest. Such forests are suitable for subsequent processing, such as (some types of) reranking or LFG f-structure computation, that can be performed ontop of a shared forest, but that may have a high (e.g., exponential) complexity w.r.t. the number of trees contained in the forest. We evaluate the performances of both algorithms on real-scale NLP forests generated with a PCFG extracted from the Penn Treebank.