Уолл-стрит Джорнал

Recent work has considered corpus-based or statistical approaches to the problem of prepositional phrase attachment ambiguity. Typically, ambiguous verb phrases of the form v rip1 p rip2 are resolved through …...

This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability es- timation techniques are extended to calcu- …...

We present an extensive empirical com- parison of several smoothing techniques in the domain of language modeling, includ- ing those described by Jelinek and Mer- cer (1980), Katz (1987), and …...

We present a knowledge and context-based system for parsing and translating natu- ral language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system …...

We present results of probabilistic tag- ging of Czech texts in order to show how these techniques work for one of the highly morphologically ambiguous inflective lan- guages. After description …...

This paper introduces a new statistical ap- proach to partitioning text automatically into coherent segments. Our approach en- lists both short-range and long-range lan- guage models to help it sniff …...

In this paper we first propose a new sta- tistical parsing model, which is a genera- tive model of lexicalised context-free gram- mar. We then extend the model to in- …...

This paper considers statistical parsing of Czech, which differs radically from English in at least two respects: (1) it is a highly inflected language, and (2) it has relatively free …...

AbstractA probabilistic method is used for word sense disambiguation where the features taken are the surrounding six words. As their surface forms are used, no syntactic or semantic analysis is …...

Many real-world texts contain tables. In order to process these texts correctly and extract the infor- mation contained within the tables, it is important to identify the presence and structure …...

We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this …...

We study the impact of richer syntac- tic dependencies on the performance of the structured language model (SLM) along three dimensions: parsing accu- racy (LP/LR), perplexity (PPL) and word- error-rate …...

This paper introduces new learning al- gorithms for natural language processing based on the perceptron algorithm. We show how the algorithms can be efficiently applied to exponential sized representa- tions …...

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 …...

Manually constructing an inventory of word senses has suffered from problems including high cost, arbitrary assignment of meaning to words, and mismatch to domains. To over- come these problems, we …...

A novel technique for maximum “a posteriori” (MAP) adaptation of maximum entropy (MaxEnt) and maximum entropy Markov models (MEMM) is presented. The technique is applied to the problem of recov- …...

Parsers are often the bottleneck for data ac- quisition, processing text too slowly to be widely applied. One way to improve the efficiency of parsers is to construct more confident …...

We evaluate the Berkeley parser on text from an online discussion forum. We evaluate the parser output with and without gold tokens and spellings (using Sparseval and Parseval), and we …...

Two of the mechanisms for creating natural transitions between adjacent sentences in a text, resulting in local coherence, involve dis- course relations and switches of focus of at- tention between …...

AbstractWe introduce a substantial update of the Prague Czech-English Dependency Treebank, a parallel corpus manually annotated at the deep syntactic layer of linguistic representation. The English part consists of the …...

AbstractCurrent supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can …...

Readability depends on many factors rang- ing from shallow features like word length to semantic ones like coherence. We intro- duce novel graph-based coherence features based on frequent subgraphs and …...

Multiword expressions (MWEs) consist of groups of tokens, which should be treated as a single syntactic or semantic unit. In this work, we focus on verbal MWEs (VMWEs), whose accurate …...

AbstractNamed entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available …...

AbstractSeveral linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency …...

AbstractUnsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating …...

AbstractWe analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing F1 score on the Wall Street Journal (WSJ) development set (1,700 sentences). We introduce …...