dependency syntax
Recently Published Documents

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurant14, Laptop, Restaurant16, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on “government” and “lockdown” of 1,658,250 tweets about “#COVID-19” that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users’ positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users’ emotions over time based on the tweets and on our models.

This article outlines the theoretical basis of describing morphosyntactic problems in The Dictionary of Proper Uses of Language. The main point was to 1) show the principles of the reference grammatical model and 2) to describe the main phases of the procedure of language facts evaluation. In the first case, we refer to the dependency syntax model, which reflects the relationships between the elements of syntactic structures through assigning them to a specific type of binding. In the second case, in contrast to the traditional nor-mative model, the importance of the frequency of the specific syntactic structures is much more underlined. Ultimately, the combination of prevalence and frequency of language elements allows for constructing an objective assessment procedure.

In order to improve the performance of Tibetan natural language processing applications such as machine translation, sentiment analysis and other tasks, this article proposes a neural network-based method for syntactic analysis of Tibetan language dependence. Part of the corpus of Qinghai Normal University’s part-of-speech tag set is marked by the corresponding mapping relationship is transformed into the corpus annotated by the national standard part-of-speech tag set. At the same time, the CoNLL format Tibetan language dependency syntax tree library is constructed, and the method of shift-reduce plus neural network is adopted to systematically study and analyze the Tibetan language dependency syntax. Thereby improving the quality of Tibetan dependency syntactic analysis, and its accuracy rate reaches UAS:94.59%

Dependency syntax has long been recognized as a crucial source of features for relation extraction. Previous work considers 1-best trees produced by a parser during preprocessing. However, error propagation from the out-of-domain parser may impact the relation extraction performance. We propose to leverage full dependency forests for this task, where a full dependency forest encodes all possible trees. Such representations of full dependency forests provide a differentiable connection between a parser and a relation extraction model, and thus we are also able to study adjusting the parser parameters based on end-task loss. Experiments on three datasets show that full dependency forests and parser adjustment give significant improvements over carefully designed baselines, showing state-of-the-art or competitive performances on biomedical or newswire benchmarks.

This paper presents the foundations, procedures, tests and first results of a dependency treebank of the Spanish Sign Language (LSE). Dependency syntax offers many advantages over other alternatives for the systematic and exhaustive syntactic analysis of a corpus. Nevertheless, the visual modality that is characteristic of sign languages poses unique challenges for their syntactic analysis, among which the most prominent is the simultaneity of expression: both hands, face and other non-manual components. Taking into account these and other particularities of sign languages, the paper explores the main difficulties faced when one tries to apply some usual categories and relations from the syntactic analysis of spoken and written languages to LSE.

This paper presents the foundations, procedures, tests and first results of a dependency treebank of the Spanish Sign Language (LSE). Dependency syntax offers many advantages over other alternatives for the systematic and exhaustive syntactic analysis of a corpus. Nevertheless, the visual modality that is characteristic of sign languages poses unique challenges for their syntactic analysis, among which the most prominent is the simultaneity of expression: both hands, face and other non-manual components. Taking into account these and other particularities of sign languages, the paper explores the main difficulties faced when one tries to apply some usual categories and relations from the syntactic analysis of spoken and written languages to LSE.