Индукция грамматики

We report work on effectively incorporating lin- guistic knowledge into grammar induction. We use a highly interactive bottom-up inductive logic programming (ILP) algorithm to learn 'missing' grammar rules from an …...

Exploiting unannotated natural language data is hard largely because unsupervised parameter estimation is hard. We describe deterministic annealing (Rose et al., 1990) as an appealing alternative to the Expectation- Maximization …...

Most statistical parsers have used the gram- mar induction approach, in which a stochastic grammar is induced from a treebank. An alter- native approach is to induce a controller for …...

We propose a new language learning model that learns a syntactic-semantic grammar from a small number of natural language strings annotated with their semantics, along with basic assumptions about natural …...

. The current state of Philippine linguistic resources, which includes formal grammars, electronic dictionaries and corpora are not yet significant to address industrial- strength language technologies. This paper discusses a …...

A central problem for NLP is grammar in- duction: the development of unsupervised learning algorithms for syntax. In this pa- per we present a lattice-theoretic represen- tation for natural language …...

We show how features can easily be added to standard generative models for unsuper- vised learning, without requiring complex new training methods. In particular, each component multinomial of a generative …...

We present an approach to grammar induc- tion that utilizes syntactic universals to im- prove dependency parsing across a range of languages. Our method uses a single set of manually-specified …...

Many statistical learning problems in NLP call for local model search methods. But accu- racy tends to suffer with current techniques, which often explore either too narrowly or too broadly: …...

Grammar induction is a basic step in natural language processing. Based on the volume of information that is used by different methods, we can distinguish three types of grammar in- …...

AbstractGrounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years. In most work on this topic, …...

In this paper, we consider the application of topic modelling to the task of induct- ing grammar rules. In particular, we look at the use of a recently developed method …...

In this paper, we demonstrate that significant performance gains can be achieved in CCG semantic parsing by introducing a linguistically moti- vated grammar induction scheme. We present a new morpho-syntactic …...

We present a simple, data-driven approach to generation from knowledge bases (KB). A key feature of this approach is that grammar induction is driven by the ex- tended domain of …...

We describe the grammar induction sys- tem for Spoken Dialogue Systems (SDS) submitted to SemEval’14: Task 2. A sta- tistical model is trained with a rich fea- ture set and …...

We propose a grammar induction tech- nique for AMR semantic parsing. While previous grammar induction techniques were designed to re-learn a new parser for each target application, the recently anno- …...

AbstractGrammar induction is the task of learning syntactic structure in a setting where that structure is hidden. Grammar induction from words alone is interesting because it is similiar to the …...

Center-embedding is difficult to process and is known as a rare syntactic construction across languages. In this paper we describe a method to incorporate this assumption into the gram- mar …...

AbstractWe show how to predict the basic word-order facts of a novel language given only a corpus of part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, …...

AbstractGrammar induction is the task of learning syntactic structure without the expert-labeled treebanks (Charniak and Carroll, 1992; Klein and Manning, 2002). Recent work on latent tree learning offers a new …...

AbstractThere has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, …...

AbstractIn this paper we demonstrate that context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies. This contrasts to the most popular current methods for grammar …...

AbstractMost previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded …...

AbstractRecent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task. Despite their high accuracy in identifying low-level structures, prior arts tend …...

AbstractThis paper analyses the degree to which dialect classifiers based on syntactic representations remain stable over space and time. While previous work has shown that the combination of grammar induction …...

AbstractWe study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and …...