During early language acquisition, infants must learn both a lexicon and a model of phonetics that explains how lexical items can vary in pronunciation—for instance “the” might be realized as [Di] or [D@]. Previous models of acquisition have generally tackled these problems in isolation, yet behavioral evidence suggests infants acquire lexical and phonetic knowledge simultaneously.
We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. Parameter search with EM produces higher quality analyses than previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher F1 of 71% on nontrivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to the basic model.
In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar. We then extend the model to include a probabilistic treatment of both subcategorisation and wh-movement. Results on Wall Street Journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average improvement of 2.3% over (Collins 96). is derived from the analysis given in Generalized Phrase Structure Grammar (Gazdar et al. 95).
This paper presents a translation model that is based on tree sequence alignment, where a tree sequence refers to a single sequence of subtrees that covers a phrase. The model leverages on the strengths of both phrase-based and linguistically syntax-based method. It automatically learns aligned tree sequence pairs with mapping probabilities from word-aligned biparsed parallel texts.
This paper presents a tripartite model of dialogue in which three different kinds of actions are modeled: domain actions, problem-solving actions, and discourse or communicative actions. We contend that our process model provides a more finely differentiated representation of user intentions than previous models; enables the incremental recognition of communicative actions that cannot be recognized from a single utterance alone; and accounts for implicit acceptance of a communicated proposition. ...
Previous models of discourse have inadequately accounted for how beliefs change during a conversation. This paper outlines a model of dialogue which maintains and updates a user's multi-level belief model as the discourse proceeds. This belief model is used in a plan-recognition framework to identify communicative goals such as expressing surprise.
Two apparently opposing DOP models exist in the literature: one which computes the parse tree involving the most frequent subtrees from a treebank and one which computes the parse tree involving the fewest subtrees from a treebank. This paper proposes an integration of the two models which outperforms each of them separately. Together with a PCFGreduction of DOP we obtain improved accuracy and efficiency on the Wall Street Journal treebank Our results show an 11% relative reduction in error rate over previous models, and an average processing time of 3.6 seconds per WSJ sentence. ...
This paper presents a new, exemplar-based model of thematic ﬁt. In contrast to previous models, it does not approximate thematic ﬁt as argument plausibility or ‘ﬁt with verb selectional preferences’, but directly as semantic role plausibility for a verb-argument pair, through similaritybased generalization from previously seen verb-argument pairs. This makes the model very robust for data sparsity. We argue that the model is easily extensible to a model of semantic role ambiguity resolution during online sentence comprehension. ...
The credit derivatives market is booming and, for the first time, expanding into the banking sector which previously has had very little exposure to quantitative modeling. This phenomenon has forced a large number of professionals to confront this issue for the first time. Credit Derivatives Pricing Models provides an extremely comprehensive overview of the most current areas in credit risk modeling as applied to the pricing of credit derivatives.
Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
As I am finishing this book, Science magazine is running a special issue about the
sequencing of the macaque genome. It turns out that macaques share about 93 percent
of their genes with us, humans. Previously it has been already reported that
chimpanzees share about 96 percent of their genes with us. Yes, the macaque is our
common ancestor, and it might be expected that, together with the chimps, we continued
with our natural selection some 23 million years ago until, some 6 million
years ago, we departed from the chimps to continue our further search for better
The new monograph ‘‘Mathematical Modeling in Mechanics of Granular Mate-
rials’’ written by Oxana & Vladimir Sadovskii is based on a previous Russian
version published in 2008. The Russian version was significantly revised and
extended. The References were updated with respect to the readers not being
familiar with the Russian language. Instead of eight chapters of the Russian ori-
ginal version there are now ten chapters—a new chapter devoted to continua with
independent rotational degrees of freedom is added....
This paper discusses a decision-tree approach to the problem of assigning probabilities to words following a given text. In contrast with previous decision-tree language model attempts, an algorithm for selecting nearly optimal questions is considered. The model is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the well-known trigram model.
Most previous studies of morphological disambiguation and dependency parsing have been pursued independently. Morphological taggers operate on n-grams and do not take into account syntactic relations; parsers use the “pipeline” approach, assuming that morphological information has been separately obtained. However, in morphologically-rich languages, there is often considerable interaction between morphology and syntax, such that neither can be disambiguated without the other.
The language model (LM) is a critical component in most statistical machine translation (SMT) systems, serving to establish a probability distribution over the hypothesis space. Most SMT systems use a static LM, independent of the source language input. While previous work has shown that adapting LMs based on the input improves SMT performance, none of the techniques has thus far been shown to be feasible for on-line systems.
Syntactic analysis inﬂuences the way in which the source sentence is translated. Previous efforts add syntactic constraints to phrase-based translation by directly rewarding/punishing a hypothesis whenever it matches/violates source-side constituents. We present a new model that automatically learns syntactic constraints, including but not limited to constituent matching/violation, from training corpus. The model brackets a source phrase as to whether it satisﬁes the learnt syntactic constraints. The bracketed phrases are then translated as a whole unit by the decoder. ...
Morphological processes in Semitic languages deliver space-delimited words which introduce multiple, distinct, syntactic units into the structure of the input sentence. These words are in turn highly ambiguous, breaking the assumption underlying most parsers that the yield of a tree for a given sentence is known in advance. Here we propose a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
This paper presents a new web mining scheme for parallel data acquisition. Based on the Document Object Model (DOM), a web page is represented as a DOM tree. Then a DOM tree alignment model is proposed to identify the translationally equivalent texts and hyperlinks between two parallel DOM trees. By tracing the identified parallel hyperlinks, parallel web documents are recursively mined. Compared with previous mining schemes, the benchmarks show that this new mining scheme improves the mining coverage, reduces mining bandwidth, and enhances the quality of mined parallel sentences.
In this paper, we present a novel global reordering model that can be incorporated into standard phrase-based statistical machine translation. Unlike previous local reordering models that emphasize the reordering of adjacent phrase pairs (Tillmann and Zhang, 2005), our model explicitly models the reordering of long distances by directly estimating the parameters from the phrase alignments of bilingual training sentences.
We present a novel OCR error correction method for languages without word delimiters that have a large character set, such as Japanese and Chinese. It consists of a statistical OCR model, an approximate word matching method using character shape similarity, and a word segmentation algorithm using a statistical language model. By using a statistical OCR model and character shape similarity, the proposed error corrector outperforms the previously published method. When the baseline character recognition accuracy is 90%, it achieves 97.4% character recognition accuracy. ...