This guide was created to assist Artists and Engineers, to learn the basics of mesh modelling of non deformable objects with 'Blender'. It uses a structured approach to introducing Blenders tools and work-methods. Following the guide should enable you to become familiar with blender and create models from the simplest of parts to complex accurate engineering assemblies and designs. The guide focuses solely on Blenders Mesh Modelling capabilities, it ignores the myriad of animation,
The first all-inclusive guidebook for designing, building, and implementing a sturdy core valuation/projection model
In today’s no-room-for-error corporate finance market, precise and effective financial modeling is essential for both determining a company’s current value and projecting its future performance. Yet few books have explained how to build models that accurately interpret a company’s financial statement, while none have focused on projection models.
(1) Since the simpler model features less regressor than the larger model, it follows that the VIF of
the simpler model will be less than that of the larger model. The reason is that the more variables
we include in the model, the greater multicollinearity, and, hence, the greater Rj
, unless the
omitted variables happen to be orthogonal to the regressors included in the simpler model. The
simpler model, which omits relevant variables, produces bias estimates but with smaller
variances. Consequently, there appears to be a tradeoff between bias and precision.
This innovative text presents computer programming as a unified discipline in a way that is both practical and scientifically sound. The book focuses on techniques of lasting value and explains them precisely in terms of a simple abstract machine. The book presents all major programming paradigms in a uniform framework that shows their deep relationships and how and where to use them together.After an introduction to programming concepts, the book presents both well-known and lesser-known computation models ("programming paradigms"). ...
Many multilingual NLP applications need to translate words between different languages, but cannot afford the computational expense of inducing or applying a full translation model. For these applications, we have designed a fast algorithm for estimating a partial translation model, which accounts for translational equivalence only at the word level . The model's precision/recall trade-off can be directly controlled via one threshold parameter. This feature makes the model more suitable for applications that are not fully statistical.
We propose a language model based on a precise, linguistically motivated grammar (a hand-crafted Head-driven Phrase Structure Grammar) and a statistical model estimating the probability of a parse tree. The language model is applied by means of an N-best rescoring step, which allows to directly measure the performance gains relative to the baseline system without rescoring. To demonstrate that our approach is feasible and beneﬁcial for non-trivial broad-domain speech recognition tasks, we applied it to a simpliﬁed German broadcast-news transcription task.
This paper presents a new model for word alignments between parallel sentences, which allows one to accurately estimate different parameters, in a computationally efficient way. An application of this model to bilingual terminology extraction, where terms are identified in one language and guessed, through the alignment process, in the other one, is also described. An experiment conducted on a small English-French parallel corpus gave results with high precision, demonstrating the validity of the model. ...
Interpreting fully natural speech is an important goal for spoken language understanding systems. However, while corpus studies have shown that about 10% of spontaneous utterances contain self-corrections, or REPAIRS, little is known about the extent to which cues in the speech signal may facilitate repair processing. We identify several cues based on acoustic and prosodic analysis of repairs in a corpus of spontaneous speech, and propose methods for exploiting these cues to detect and correct repairs.
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).
We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state ontological relations. We leverage distant supervision using relations from the knowledge base FreeBase, but do not require any manual heuristic nor manual seed list selections. Results show that the learned patterns can be used to extract new relations with good precision.
This paper presents a set of Bayesian methods for automatically extending the W ORD N ET ontology with new concepts and annotating existing concepts with generic property ﬁelds, or attributes. We base our approach on Latent Dirichlet Allocation and evaluate along two dimensions: (1) the precision of the ranked lists of attributes, and (2) the quality of the attribute assignments to W ORD N ET concepts. In all cases we ﬁnd that the principled LDA-based approaches outperform previously proposed heuristic methods, greatly improving the speciﬁcity of attributes at each concept. ...
In this work, the problem of extracting phrase translation is formulated as an information retrieval process implemented with a log-linear model aiming for a balanced precision and recall. We present a generic phrase training algorithm which is parameterized with feature functions and can be optimized jointly with the translation engine to directly maximize the end-to-end system performance. Multiple data-driven feature functions are proposed to capture the quality and conﬁdence of phrases and phrase pairs.
This paper proposes a novel method that exploits multiple resources to improve statistical machine translation (SMT) based paraphrasing. In detail, a phrasal paraphrase table and a feature function are derived from each resource, which are then combined in a log-linear SMT model for sentence-level paraphrase generation. Experimental results show that the SMT-based paraphrasing model can be enhanced using multiple resources. The phrase-level and sentence-level precision of the generated paraphrases are above 60% and 55%, respectively.
This paper proposes an alignment adaptation approach to improve domain-specific (in-domain) word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment.
We present a new approach to stochastic modeling of constraintbased grammars that is based on loglinear models and uses EM for estimation from unannotated data. The techniques are applied to an LFG grammar for German. Evaluation on an exact match task yields 86% precision for an ambiguity rate of 5.4, and 90% precision on a subcat frame match for an ambiguity rate of 25. Experimental comparison to training from a parsebank shows a 10% gain from EM training.
We study the impact of richer syntactic dependencies on the performance of the structured language model (SLM) along three dimensions: parsing accuracy (LP/LR), perplexity (PPL) and worderror-rate (WER, N-best re-scoring). We show that our models achieve an improvement in LP/LR, PPL and/or WER over the reported baseline results using the SLM on the UPenn Treebank and Wall Street Journal (WSJ) corpora, respectively. Analysis of parsing performance shows correlation between the quality of the parser (as measured by precision/recall) and the language model performance (PPL and WER). ...
We present an algorithm for computing n-gram probabilities from stochastic context-free grammars, a procedure that can alleviate some of the standard problems associated with n-grams (estimation from sparse data, lack of linguistic structure, among others). The method operates via the computation of substring expectations, which in turn is accomplished by solving systems of linear equations derived from the grammar. The procedure is fully implemented and has proved viable and useful in practice. confirming its practical feasibility and utility.
Natural langt~ages are often assumed to be constrained so that they are either easily learnable or parsdble, but few studies have investigated the conrtcction between these two "'functional'" demands, Without a fonnal model of pamtbility or learnability, it is difficult to determine which is morc "dominant" in fixing the properties of natural languages.
We present a general model and conceptual framework for specifying architectures for incremental processing in dialogue systems, in particular with respect to the topology of the network of modules that make up the system, the way information ﬂows through this network, how information increments are ‘packaged’, and how these increments are processed by the modules. This model enables the precise speciﬁcation of incremental systems and hence facilitates detailed comparisons between systems, as well as giving guidance on designing new systems. ...