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This paper presents a dependency parsing scheme using an extended finite state approach. The parser augments input representation with "channels" so that links representing syntactic dependency rela- tions among words …...

This paper describes the characteristic features of dependency structures of Japanese spoken language by investigating a spoken dialogue cor- pus, and proposes a stochastic approach to de- pendency parsing. The …...

We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product …...

This paper presents an approach to depen- dency parsing which can utilize any stan- dard machine learning (classification) al- gorithm. A decision list learner was used in this work. The …...

We present an improved approach for learning dependency parsers from tree- bank data. Our technique is based on two ideas for improving large margin train- ing in the context of …...

Integer Linear Programming has recently been used for decoding in a number of probabilistic models in order to enforce global constraints. However, in certain ap- plications, such as non-projective depen- …...

We develop dependency parsers for Ara- bic, English, Chinese, and Czech using Bayes Point Machines, a training algo- rithm which is as easy to implement as the perceptron yet competitive …...

This paper presents an online algorithm for dependency parsing problems. We propose an adaptation of the passive and aggressive online learning algorithm to the dependency parsing domain. We evaluate the …...

We present a data-driven variant of the LR algorithm for dependency parsing, and ex- tend it with a best-first search for probabil- istic generalized LR dependency parsing. Parser actions are …...

We present experiments with a dependency parsing model defined on rich factors. Our model represents dependency trees with fac- tors that include three types of relations be- tween the tokens …...

We present a novel framework that com- bines strengths from surface syntactic pars- ing and deep syntactic parsing to increase deep parsing accuracy, specifically by com- bining dependency and HPSG …...

An open issue in data-driven dependency parsing is how to handle non-projective dependencies, which seem to be required by linguistically adequate representations, but which pose problems in parsing with respect …...

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learn- ing systems on the same data sets. In 2007, as in …...

We define a new formalism, based on Sikkel’s parsing schemata for constituency parsers, that can be used to describe, analyze and com- pare dependency parsing algorithms. This abstraction allows us …...

This paper proposes an approach using large scale case structures, which are automatically constructed from both a small tagged corpus and a large raw cor- pus, to improve Chinese dependency …...

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for …...

This paper presents an effective dependency parsing approach of incorporating short de- pendency information from unlabeled data. The unlabeled data is automatically parsed by a deterministic dependency parser, which can …...

Previous studies of data-driven dependency parsing have shown that the distribution of parsing errors are correlated with theoretical properties of the models used for learning and inference. In this paper, …...

In this paper, we focus on the adaptation problem that has a large labeled data in the source domain and a large but unlabeled data in the target domain. Our …...

This article investigates the effect of a set of linguistically motivated features on ar- gument disambiguation in data-driven de- pendency parsing of Swedish. We present results from experiments with gold …...

We present a simple and effective semi- supervised method for training dependency parsers. We focus on the problem of lex- ical representation, introducing features that incorporate word clusters derived from …...

We explore a stacked framework for learn- ing to predict dependency structures for natu- ral language sentences. A typical approach in graph-based dependency parsing has been to assume a factorized …...

This paper presents a comparative evalua- tion of several state-of-the-art English parsers based on different frameworks. Our approach is to measure the impact of each parser when it is used …...

This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning ap- proach. We describe an extension of semi- supervised structured conditional models (SS-SCMs) to the …...

We investigate the possibility of exploit- ing character-based dependency for Chi- nese information processing. As Chinese text is made up of character sequences rather than word sequences, word in Chi- …...

We present an asymmetric approach to a run- time combination of two parsers where one component serves as a predictor to the other one. Predictions are integrated by means of …...

Building on work detecting errors in de- pendency annotation, we set out to correct local dependency errors. To do this, we outline the properties of annotation errors that make the …...

We present a comparison between two systems for establishing syntactic and semantic depen- dencies: one that performs dependency parsing and semantic role labeling as a single task, and another that …...

Generic relation identification (GRI) aims to build models of relation-forming entity pairs that can be transferred across domains without modification of model parameters. GRI has high utility in terms of …...

This paper presents a set of experiments performed on parsing the Basque Dependency Treebank. We have concentrated on treebank transformations, maintaining the same basic parsing algorithm across the experiments. The …...

This paper presents a simple and effective approach to improve dependency parsing by using subtrees from auto-parsed data. First, we use a baseline parser to parse large-scale unannotated data. Then …...

Even leaving aside concerns of cognitive plausibil- ity, incremental parsing is appealing for applications such as speech recognition and machine translation be- cause it could allow the incorporation of syntactic …...

An open problem in dependency parsing is the accurate and efficient treatment of non-projective structures. We propose to attack this problem using chart-parsing algorithms developed for mildly context- sensitive grammar …...

This paper describes a search procedure to discover optimal feature sets for depen- dency parsers. The search applies to the shift–reduce algorithm and the feature sets are extracted from the …...

This paper proposes a dependency parsing method that uses bilingual constraints to improve the accuracy of parsing bilingual texts (bitexts). In our method, a target- side tree fragment that corresponds …...

In this paper we describe an intuitionistic method for dependency parsing, where a classifier is used to determine whether a pair of words forms a dependency edge. And we also …...

Perceptron training is widely applied in the natural language processing community for learning complex structured models. Like all structured prediction learning frameworks, the structured perceptron can be costly to train …...

Previous work on dependency parsing used various kinds of combination models but a systematic analysis and comparison of these approaches is lacking. In this paper we imple- mented such a …...

We evaluate two dependency parsers, MSTParser and MaltParser, with respect to their capacity to recover unbounded de- pendencies in English, a type of evalu- ation that has been applied to …...

AbstractIn this paper, we describe our hybrid parsing model on the Mandarin Chinese processing. In particular, we work on the Tsinghua Chinese Treebank (TCT), whose annotation has both constitutes and …...

AbstractWe examine the performance of three dependency parsing systems, in particular, their performance variation across Wikipedia domains. We assess the performance variation of (i) Alpino, a deep grammar-based system coupled …...

The paper describes a data driven dependency parsing approach which uses clausal informa- tion of a sentence to improve the parser per- formance. The clausal information is added automatically during …...

We reveal a previously unnoticed connection between dependency parsing and statistical machine translation (SMT), by formulating the dependency parsing task as a problem of word alignment. Furthermore, we show that …...

This paper proposes an efficient online method that trains a classifier with many conjunctive features. We employ kernel computation called kernel slicing, which explicitly considers conjunctions among frequent features in …...

AbstractWe investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and …...

We show how web mark-up can be used to improve unsupervised dependency pars- ing. Starting from raw bracketings of four common HTML tags (anchors, bold, ital- ics and underlines), we …...

Martins et al. (2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLL- X Shared Task datasets. The paper shows how …...

AbstractWe present a study that compares data-driven dependency parsers obtained by means of annotation projection between language pairs of varying structural similarity. We show how the partial dependency trees projected …...

We present an approach to grammar induc- tion that utilizes syntactic universals to im- prove dependency parsing across a range of languages. Our method uses a single set of manually-specified …...

Dual decomposition has been recently pro- posed as a way of combining complemen- tary models, with a boost in predictive power. However, in cases where lightweight decom- positions are not …...

We describe a generative model for non- projective dependency parsing based on a sim- plified version of a transition system that has recently appeared in the literature. We then develop …...

We explore the contribution of morphologi- cal features – both lexical and inflectional – to dependency parsing of Arabic, a morpho- logically rich language. Using controlled ex- periments, we find …...

This paper shows that the performance of history-based models can be significantly im- proved by performing lookahead in the state space when making each classification deci- sion. Instead of simply …...

Dependency parsing is a central NLP task. In this paper we show that the common eval- uation for unsupervised dependency parsing is highly sensitive to problematic annotations. We show that …...

We show how punctuation can be used to im- prove unsupervised dependency parsing. Our linguistic analysis confirms the strong connec- tion between English punctuation and phrase boundaries in the Penn …...

We use an automatic pipeline of word tokenization, stemming, POS tagging, and vocalization to perform real-world Arabic dependency parsing. In spite of the high accuracy on the modules, the very …...

We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard su- pervised parsing …...

The standard training regime for transition-based dependency parsers makes use of an oracle, which predicts an optimal transition sequence for a sentence and its gold tree. We present an improved …...

Employing higher-order subtree structures in graph-based dependency parsing has shown substantial improvement over the accuracy, however suffers from the inefficiency increasing with the order of subtrees. We present a new …...

Recent study shows that parsing accuracy can be largely improved by the joint optimization of part-of-speech (POS) tagging and dependency parsing. However, the POS tagging task does not benefit much …...

Hungarian is a stereotype of morpholog- ically rich and non-configurational lan- guages. Here, we introduce results on de- pendency parsing of Hungarian that em- ploy a 80K, multi-domain, fully manu- …...

The easy-first non-directional dependency parser has demonstrated its advantage over transition based dependency parsers which parse sentences from left to right. This work investigates easy-first method on Chinese POS tagging, …...

We study the problem of classifying the temporal relationship between events and time expres- sions in text. In contrast to previous methods that require extensive feature engineering, our approach is …...

Recently there has been substantial interest in using spectral methods to learn generative se- quence models like HMMs. Spectral meth- ods are attractive as they provide globally con- sistent estimates …...

Previous joint models of Chinese part-of-speech (POS) tagging and dependency parsing are extended from either graph- or transition-based dependency models. Our analysis shows that the two models have different error …...

We investigate the stacking of dependency and phrase structure parsers, i.e. we define features from the output of a phrase structure parser for a dependency parser and vice versa. Our …...

We introduce lightly supervised learning for dependency parsing. In this paradigm, the al- gorithm is initiated with a parser, such as one that was built based on a very limited …...

Coarse-to-fine inference has been shown to be a robust approximate method for improving the efficiency of structured prediction models while preserving their accuracy. We propose a multi-pass coarse-to-fine architecture for …...

AbstractDue to the nature of complex NLP problems, structured prediction algorithms have been important modeling tools for a wide range of tasks. While there exists evidence showing that linear Structural …...

Feature computation and exhaustive search have significantly restricted the speed of graph-based dependency parsing. We propose a faster framework of dynamic feature selec- tion, where features are added sequentially as …...

AbstractThis paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed …...

AbstractJoint morphological and syntactic analysis has been proposed as a way of improving parsing accuracy for richly inflected languages. Starting from a transition-based model for joint part-of-speech tagging and dependency …...

Hungarian is the stereotype of morpholog- ically rich and free word order languages. Here, we introduce magyarlanc, a nat- ural language toolkit developed for the linguistic preprocessing – segmentation, morphological …...

Many models in NLP involve latent vari- ables, such as unknown parses, tags, or alignments. Finding the optimal model pa- rameters is then usually a difficult noncon- vex optimization problem. …...

In current dependency parsing models, con- ventional features (i.e. base features) defined over surface words and part-of-speech tags in a relatively high-dimensional feature space may suffer from the data sparseness …...

We present a novel approach, called selec- tional branching, which uses confidence es- timates to decide when to employ a beam, providing the accuracy of beam search at speeds close …...

This paper presents a non-projective dependency parsing system that is transition-based and op- erates in three steps. The three steps include one classical method for projective dependency parsing and two …...

This paper proposes a simple yet effective framework for semi-supervised dependency parsing at entire tree level, referred to as ambiguity-aware ensemble training. Instead of only using 1- best parse trees …...

AbstractWe present a new version of the Croatian Dependency Treebank. It constitutes a slight departure from the previously closely observed Prague Dependency Treebank syntactic layer annotation guidelines as we introduce …...

Dependency parsing is a core task in NLP, and it is widely used by many applica- tions such as information extraction, ques- tion answering, and machine translation. In the era …...

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 …...

For languages such as English, several constituent-to-dependency conversion schemes are pro- posed to construct corpora for dependency parsing. It is hard to determine which scheme is better because they reflect …...

Accurate scoring of syntactic structures such as head-modifier arcs in dependency parsing typically requires rich, dimensional feature representations.high- small subset of such features is often se-A lected manually. This is …...

We present a multi-view annotation framework for Chinese treebanking, which uses dependen- cy structures as the base view and supports conversion into phrase structures with minimal loss of information. A …...

AbstractThis paper addresses the problems of out-of-vocabulary (OOV) words, named entities in particular, in dependency parsing. The OOV words, whose word forms are unknown to the learning-based parser, in a …...

Much of the recent work on depen- dency parsing has been focused on solv- ing inherent combinatorial problems as- sociated with rich scoring functions. In contrast, we demonstrate that highly …...

There has been considerable work on syn- tactic language models and they have ad- vanced greatly over the last decade. Most of them have used a probabilistic context- free grammar …...

AbstractThe Norwegian Dependency Treebank is a new syntactic treebank for Norwegian Bokmäl and Nynorsk with manual syntactic and morphological annotation, developed at the National Library of Norway in collaboration with …...

AbstractWe present SETimes.HR ― the first linguistically annotated corpus of Croatian that is freely available for all purposes. The corpus is built on top of the SETimes parallel corpus of …...

Cross-lingual learning has become a popu- lar approach to facilitate the development of resources and tools for low-density lan- guages. Its underlying idea is to make use of existing tools …...

AbstractSo far predicted scenarios for Turkish dependency parsing have used a morphological disambiguator that is trained on the data distributed with the tool(Sak et al., 2008). Although models trained on …...

Accurate dependency parsing requires large treebanks, which are only available for a few languages. We propose a method that takes advantage of shared structure across languages to build a mature …...

Existing semantic parsing research has steadily improved accuracy on a few do- mains and their corresponding meaning representations. In this paper, we present a novel supervised semantic parsing algo- rithm, …...

Recent years have seen increased interest in text normalization in social media, as the in- formal writing styles found in Twitter and other social media data often cause problems for …...

Efficient music information retrieval (MIR) require to have meta information about music along with content based information in the knowledge base. Dis- cussion forums on music are rich sources of …...

toIn this paper we present the first ever, the best of our knowledge, dis- course parser for multi-party chat dia- logues. Discourse in multi-party dia- logues dramatically differs from mono- …...

This paper presents the approach of the GTI Research Group to SemEval-2015 task 10 on Sentiment Analysis in Twitter, or more specif- ically, subtasks A (Contextual Polarity Disam- biguation) and …...

We extend and improve upon recent work in struc- tured training for neural network transition-based dependency parsing. We do this by experimenting with novel features, additional transition systems and by …...

We present extensions to a continuous- state dependency parsing method that makes it applicable to morphologically rich languages. Starting with a high- performance transition-based parser that uses long short-term memory …...

In this paper, we introduce a new approach for joint segmentation, POS tagging and de- pendency parsing. While joint modeling of these tasks addresses the issue of error prop- agation …...

A common approach to dependency parsing is scoring a parse via a linear function of a set of indicator features. These features are typically manually constructed from templates that are …...

Recently, neural network based depen- dency parsing has attracted much interest, which can effectively alleviate the prob- lems of data sparsity and feature engineer- ing by using the dense features. …...

We present a transition-based arc-eager model to parse spinal trees, a dependency- based representation that includes phrase- structure information in the form of con- stituent spines assigned to tokens. As …...

We present two simple modifications to the models in the popular Word2Vec tool, in or- der to generate embeddings more suited to tasks involving syntax. The main issue with the …...

AbstractIn Computational Linguistics, Hindi and Urdu are not viewed as a monolithic entity and have received separate attention with respect to their text processing. From part-of-speech tagging to machine translation, …...

AbstractWe propose a dependency parsing pipeline, in which the parsing of long-distance projections and localized dependencies are explicitly decomposed at the input level. A chosen baseline dependency parsing model performs …...

AbstractVarious treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain common syntactic knowledge that is potential to …...

Different from traditional active learning based on sentence-wise full annotation (FA), this paper proposes active learning with dependency-wise partial annotation (PA) as a finer-grained unit for dependency parsing. At each …...

We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the for- ward and backward parsing directions. The parsing procedure for each …...

We explore the consequences of representing token segmentations as hierarchical structures (trees) for the task of Multiword Expression (MWE) recognition, in isolation or in com- bination with dependency parsing. We …...

AbstractIn this paper, we focus on the verb-particle (V-Prt) split construction in English and German and its difficulty for parsing and Machine Translation (MT). For German, we use an existing …...

AbstractTransition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art. In this paper, we propose a …...

This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specif- ically, subtasks A (Message Polarity Classifi- cation), B …...

In this paper we present the first, to the best of our knowledge, discourse parser that is able to predict non-tree DAG structures. We use Integer Linear Programming (ILP) to …...

AbstractWe focus on the improvement of accuracy of raw text parsing, from the viewpoint of language resource addition. In Japanese, the raw text parsing is divided into three steps: word …...

AbstractWe train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language …...

AbstractThe growing work in multi-lingual parsing faces the challenge of fair comparative evaluation and performance analysis across languages and their treebanks. The difficulty lies in teasing apart the properties of …...

AbstractThe paper contains a description of OPFI: Opinion Finder for the Polish Language, a freely available tool for opinion target extraction. The goal of the tool is opinion finding: a …...

Parsing accuracy using efficient greedy transi- tion systems has improved dramatically in re- cent years thanks to neural networks. Despite striking results in dependency parsing, how- ever, neural models have …...

Traditional syntax models typically lever- age part-of-speech (POS) information by constructing features from hand-tuned templates. We demonstrate that a better approach is to utilize POS tags as a reg- ularizer …...

We introduce the Treebank of Learner En- glish (TLE), the first publicly available syntactic treebank for English as a Sec- ond Language (ESL). The TLE provides manually annotated POS tags …...

AbstractThis paper studies cross-lingual transfer for dependency parsing, focusing on very low-resource settings where delexicalized transfer is the only fully automatic option. We show how to boost parsing performance by …...

AbstractThis paper proposes a generic method for the comparative evaluation of system outputs. The approach is able to quantify the pairwise differences between two outputs and to unravel in detail …...

AbstractWe introduce a novel sub-character architecture that exploits a unique compositional structure of the Korean language. Our method decomposes each character into a small set of primitive phonetic units called …...

In this paper we describe a comparison of two annotation schemes for de- pendency parsing of North Sámi, a Finno-Ugric language spoken in the north of Scandinavia and Finland. The …...

AbstractConventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in …...

AbstractAlthough sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a …...

AbstractSinglish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We …...

We present DEPCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences …...

Web 2.0 has brought with it numerous user-produced data revealing one’s thoughts, experiences, and knowledge, which are a great source for many tasks, such as information extraction, and knowledge base …...

We present TIGER 2.2-doc – a new set of annotations for the German TIGER corpus. The set moves the corpus to a document level. It includes a full mapping of …...

The paper presents a method for parsing low-resource languages with very small training corpora using multilingual word embeddings and annotated corpora of larger languages. The study demonstrates that specific language …...

AbstractWe present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal …...

AbstractIn this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes …...

In this paper we present the annotation scheme and parser results of the animacy feature in Russian and Arabic, two morphologically- rich languages, in the spirit of the universal dependency …...

AbstractCode-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP …...

In this study, we investigated unsupervised learning based Korean word sense disambiguation (WSD) using CoreNet, a Korean lexical semantic network. To facilitate the application of WSD to practical natural language …...

This platform includes two main concepts. One, provide multifaceted and deep evaluation for users of Natural Language Processing (NLP) System (such as Syntaxnet or CoreNLP), to help them choose the …...

We propose the first multi-task learning model for joint Vietnamese word segmentation, part- of-speech (POS) tagging and dependency parsing. In particular, our model extends the BIST graph-based dependency parser (Kiper- …...

AbstractThe use of subword-level information (e.g., characters, character n-grams, morphemes) has become ubiquitous in modern word representation learning. Its importance is attested especially for morphologically rich languages which generate a …...

AbstractWe introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning …...

AbstractLearning effective representations of sentences is one of the core missions of natural language understanding. Existing models either train on a vast amount of text, or require costly, manually curated …...

AbstractWe investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency …...

AbstractIn standard NLP pipelines, morphological analysis and disambiguation (MA&D) precedes syntactic and semantic downstream tasks. However, for languages with complex and ambiguous word-internal structure, known as morphologically rich languages (MRLs), …...

AbstractLack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews. To alleviate this problem, we first propose an algorithm …...

AbstractWe introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word …...

AbstractObtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to …...

AbstractThe existence of universal models to describe the syntax of languages has been debated for decades. The availability of resources such as the Universal Dependencies treebanks and the World Atlas …...

AbstractWe introduce TWT; a new treebank for Turkish which consists of web and Wikipedia sentences that are annotated for segmentation, morphology, part-of-speech and dependency relations. To date, it is the …...

AbstractChinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined at the word-level. Therefore word segmentation is the precondition of …...

AbstractIn Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where …...

AbstractSyntactic information is essential for both sentiment analysis(SA) and aspect-based sentiment analysis(ABSA). Previous work has already achieved great progress utilizing Graph Convolutional Network(GCN) over dependency tree of a sentence. However, …...

AbstractWe define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a theoretical relation between …...

AbstractBuilding an effective adversarial attacker and elaborating on countermeasures for adversarial attacks for natural language processing (NLP) have attracted a lot of research in recent years. However, most of the …...

AbstractNamed entities pose a unique challenge to traditional methods of language modeling. While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies …...

AbstractRecent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings. Thus self-training, which is effective for these …...

AbstractWe present an annotated corpus of English cooking recipe procedures, and describe and evaluate computational methods for learning these annotations. The corpus consists of 300 recipes written by members of …...

AbstractNatural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, …...

AbstractCommitments and requests are a hallmark of collaborative communication, especially in team settings. Identifying specific tasks being committed to or request from emails and chat messages can enable important downstream …...

AbstractRecent work has shown that neural rerankers can improve results for dependency parsing over the top k trees produced by a base parser. However, all neural rerankers so far have …...

AbstractLexical normalization is the task of translating non-standard social media data to a standard form. Previous work has shown that this is beneficial for many downstream tasks in multiple languages. …...

AbstractBy introducing a small set of additional parameters, a probe learns to solve specific linguistic tasks (e.g., dependency parsing) in a supervised manner using feature representations (e.g., contextualized embeddings). The …...

AbstractMannual annotation for dependency parsing is both labourious and time costly, resulting in the difficulty to learn practical dependency parsers for many languages due to the lack of labelled training …...

AbstractCross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. We point at a factor not …...

AbstractModeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, …...

AbstractTransition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task. …...

AbstractIn this paper, we introduce the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. The full test suite consists of 18,502 transitive clauses with manual …...

AbstractThe current natural language processing is strongly focused on raising accuracy. The progress comes at a cost of super-heavy models with hundreds of millions or even billions of parameters. However, …...

AbstractModern neural approaches to dependency parsing are trained to predict a tree structure by jointly learning a contextual representation for tokens in a sentence, as well as a head–dependent scoring …...

AbstractThis paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph. It details the extraction of contribution sentences and scientific entities and their relations from scholarly …...

AbstractAspect-level sentiment analysis (ASA) has received much attention in recent years. Most existing approaches tried to leverage syntactic information, such as the dependency parsing results of the input text, to …...

AbstractZero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of …...

AbstractInterpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency …...

AbstractDifferent linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of the token arcs …...

AbstractThe connection between the maximum spanning tree in a directed graph and the best dependency tree of a sentence has been exploited by the NLP community. However, for many dependency …...

AbstractThe adaptation of pretrained language models to solve supervised tasks has become a baseline in NLP, and many recent works have focused on studying how linguistic information is encoded in …...

AbstractWe compare two orthogonal semi-supervised learning techniques, namely tri-training and pretrained word embeddings, in the task of dependency parsing. We explore language-specific FastText and ELMo embeddings and multilingual BERT embeddings. …...

AbstractKnowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The objective function of knowledge …...

AbstractPeer feedback in online education becomes increasingly important to meet the demand for feedback in large scale classes, such as e.g. Massive Open Online Courses (MOOCs). However, students are often …...

AbstractWeighted pushdown automata (WPDAs) are at the core of many natural language processing tasks, like syntax-based statistical machine translation and transition-based dependency parsing. As most existing dynamic programming algorithms are …...

AbstractSocial media data such as Twitter messages (“tweets”) pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. Tasks such as Named Entity Recognition (NER) …...

AbstractAnnotation inconsistencies between data sets can cause problems for low-resource NLP, where noisy or inconsistent data cannot be easily replaced. We propose a method for automatically detecting annotation mismatches between …...

AbstractGraph-based methods, which decompose the score of a dependency tree into scores of dependency arcs, are popular in dependency parsing for decades. Recently, (CITATION) propose a headed-span-based method that decomposes …...

AbstractCompositionality— the ability to combine familiar units like words into novel phrases and sentences— has been the focus of intense interest in artificial intelligence in recent years. To test compositional …...

AbstractHigher-order methods for dependency parsing can partially but not fully address the issue that edges in dependency trees should be constructed at the text span/subtree level rather than word level. …...

AbstractGreedy algorithms for NLP such as transition-based parsing are prone to error propagation. One way to overcome this problem is to allow the algorithm to backtrack and explore an alternative …...

AbstractWhile neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational …...

AbstractDependency parsing is an important fundamental natural language processing task which analyzes the syntactic structure of an input sentence by illustrating the syntactic relations between words. To improve dependency parsing, …...

AbstractAs input representation for each sub-word, the original BERT architecture proposes the sum of the sub-word embedding, position embedding and a segment embedding. Sub-word and position embeddings are well-known and …...

AbstractIn this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types …...

AbstractProbing has become an important tool for analyzing representations in Natural Language Processing (NLP). For graphical NLP tasks such as dependency parsing, linear probes are currently limited to extracting undirected …...

AbstractData-hungry deep neural networks have established themselves as the de facto standard for many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they …...

The grammatical analysis of texts in any written language typically involves a num- ber of basic processing tasks, such as tok- enization, morphological tagging, and de- pendency parsing. State-of-the-art systems …...