Эффективность

This paper describes Picky, a probabilistic agenda-based chart parsing algorithm which uses a technique called p~'ob- abilistic prediction to predict which grammar rules are likely to lead to an acceptable …...

This paper is on dividing non-separated language sen- tences (whose words are not separated from each other with a space or other separaters) into morphemes using statistical information, not grammatical …...

This paper describes results achieved in a project which addresses the issue of how the gap between unification-based grammars as a scientific concept and real world applications can be narrowed …...

We formalize weighted dependency pars- ing as searching for maximum spanning trees (MSTs) in directed graphs. Using this representation, the parsing algorithm of Eisner (1996) is sufficient for search- ing …...

In this paper we show that generative models are competitive with and some- times superior to discriminative models, when both kinds of models are allowed to learn structures that are …...

When designing grammars of natural lan- guage, typically, more than one formal anal- ysis can account for a given phenomenon. Moreover, because analyses interact, the choices made by the engineer …...

This article tackles the Authorship Attri- bution task according to the language in- dependence issue. We propose an alterna- tive of variable length character n-grams features in supervised methods: maximal …...

AbstractAutomatic Term Extraction (ATE) or Recognition (ATR) is a fundamental processing step preceding many complex knowledge engineering tasks. However, few methods have been implemented as public tools and in particular, …...

Deep inference on a large-scale knowledge base (KB) needs a mass of formulas, but it is almost impossible to create all formulas man- ually. Data-driven methods have been pro- posed …...

We introduce several probabilistic models for learning the lexicon of a semantic parser. Lex- icon learning is the first step of training a se- mantic parser for a new application …...

AbstractWe propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using …...

AbstractStoryline generation aims to extract events described on news articles under a certain news topic and reveal how those events evolve over time. Most approaches to storyline generation first train …...

AbstractTree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only …...

AbstractHow can people communicate successfully while keeping resource costs low in the face of ambiguity? We present a principled theoretical analysis comparing two strategies for disambiguation in communication: (i) pragmatic …...

AbstractWe propose a method for program generation based on semantic scaffolds, lightweight structures representing the high-level semantic and syntactic composition of a program. By first searching over plausible scaffolds then …...

AbstractIn the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, …...

AbstractAutomatic summarization methods are efficient but can suffer from low quality. In comparison, manual summarization is expensive but produces higher quality. Can humans and AI collaborate to improve summarization performance? …...

AbstractThe multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another …...

AbstractThis study aims to evaluate three most popular word segmentation tool for a large Traditional Chinese corpus in terms of their efficiency, resource consumption, and cost. Specifically, we compare the …...

AbstractCurrent state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. …...

AbstractSupersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new state-of-the-art (SOTA). Rather than pursuing the reachless SOTA accuracy, more and more …...

AbstractOpen domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language processing (NLP). Recent works …...