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We present an effective training al- gorithm for linearly-scored dependency parsers that implements online large- margin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient …...

Several recently reported techniques for the automatic acquisition of Information Extraction (IE) systems have used depen- dency trees as the basis of their extrac- tion pattern representation. These ap- proaches …...

We present a simple but very effective ap- proach to identifying high-quality data in noisy data sets for structured problems like parsing, by greedily exploiting partial struc- tures. We analyze …...

We propose a novel algorithm for extract- ing dependencies from the derivations of a large fragment of CCG. Unlike earlier proposals, our dependency structures are always tree-shaped. We then use …...

AbstractIn this paper, we present the first results of the parallel Czech discourse annotation in the Prague Dependency Treebank 2.0. Having established an annotation scenario for capturing semantic relations crossing …...

Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In …...

This study proposes a text summarization model that simultaneously performs sen- tence extraction and compression. We translate the text summarization task into a problem of extracting a set of depen- …...

We present a novel translation model, which simultaneously exploits the constituency and dependency trees on the source side, to com- bine the advantages of two types of trees. We take …...

Recent work on Chinese analysis has led to large-scale annotations of the internal structures of words, enabling character- level analysis of Chinese syntactic struc- tures. In this paper, we investigate …...

Scrambling is acceptable reordering of verb arguments in languages such as Japanese and German. In automatic eval- uation of translation quality, BLEU is the de facto standard method, but BLEU …...

Open Relation Extraction (ORE) over- comes the limitations of traditional IE techniques, which train individual extrac- tors for every single relation type. Sys- tems such as ReVerb, PATTY, OLLIE, and …...

We describe the submissions of ILLC UvA to the metrics and tuning tasks on WMT15. Both submissions are based on the BEER evaluation metric origi- nally presented on WMT14 (Stanojevi´c …...

This paper proposes a tree-based con- volutional neural network (TBCNN) for discriminative sentence modeling. Our model leverages either constituency trees or dependency trees of sentences. The tree-based convolution process extracts …...

We present experiments with generative models for linearization of unordered la- beled syntactic dependency trees (Belz et al., 2011; Rajkumar and White, 2014). Our linearization models are derived from generative …...

Syntactic parsing of web queries is impor- tant for query understanding. However, web queries usually do not observe the grammar of a written language, and no labeled syntactic trees for …...

AbstractWe present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we …...

Treebanks exist for many different languages, but they are often quite limited in terms of size, genre, and topic coverage. It is difficult to expand these treebanks or to develop …...

AbstractSentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments. Surface syntax representations of such sentences …...

AbstractWe propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works …...

AbstractWe propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our …...

AbstractWe present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, …...

AbstractAutomated discovery of causal relationships from text is a challenging task. Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the …...

AbstractPre-trained language models such as BERT have achieved the state-of-the-art performance on natural language inference (NLI). However, it has been shown that such models can be tricked by variations of …...

AbstractThis paper studies the task of Relation Extraction (RE) that aims to identify the semantic relations between two entity mentions in text. In the deep learning models for RE, it …...

AbstractWe reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account …...

AbstractThe research presented in this paper aims at enriching the manually morphosyntactically annotated part of National Corpus of Polish (NKJP1M) with a syntactic layer, i.e. dependency trees of sentences, and …...

AbstractThis work focuses on analyzing the form and extent of syntactic abstraction captured by BERT by extracting labeled dependency trees from self-attentions. Previous work showed that individual BERT heads tend …...

AbstractUnlike English letters, Chinese characters have rich and specific meanings. Usually, the meaning of a word can be derived from its constituent characters in some way. Several previous works on …...

AbstractIt has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees. We believe this is because both types of features …...

AbstractSyntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. …...

AbstractMuch recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) …...

AbstractAspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can …...

AbstractAspect-based sentiment analysis is a fine-grained sentiment classification task. Recently, graph neural networks over dependency trees have been explored to explicitly model connections between aspects and opinion words. However, the …...

AbstractRecent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks (GNN), but these approaches are usually vulnerable to …...

AbstractOpen Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models were developed based on syntactic structure of sentence, identified by syntactic parsers. However, …...