8 research outputs found
Answer Acquisition for Knowledge Base Question Answering Systems Based on Dynamic Memory Network
In recent years, with the rapid growth of Artificial Intelligence (AI) and the Internet of Things (IoT), the question answering systems for human-machine interaction based on deep learning have become a research hotspot of the IoT. Different from the structured query method in traditional Knowledge Base Question Answering (KBQA) systems based on templates or rules, representation learning is one of the most promising approaches to solving the problems of data sparsity and semantic gaps. In this paper, an answer acquisition method for KBQA systems based on a dynamic memory network is proposed, in which representation learning is employed to represent the natural language questions that are raised by users and the knowledge base subgraphs of the related entities. These representations are taken as inputs of the dynamic memory network. The correct answers are obtained by utilizing the memory and inferential capabilities. The experimental results demonstrate the effectiveness of the proposed approach. - 2013 IEEE.This work was supported by the National Science Foundation of China under Grant 61365010.Scopu
Negation, Coordination, and Quantifiers in Contextualized Language Models
With the success of contextualized language models, much research explores
what these models really learn and in which cases they still fail. Most of this
work focuses on specific NLP tasks and on the learning outcome. Little research
has attempted to decouple the models' weaknesses from specific tasks and focus
on the embeddings per se and their mode of learning. In this paper, we take up
this research opportunity: based on theoretical linguistic insights, we explore
whether the semantic constraints of function words are learned and how the
surrounding context impacts their embeddings. We create suitable datasets,
provide new insights into the inner workings of LMs vis-a-vis function words
and implement an assisting visual web interface for qualitative analysis
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
Quotation extraction aims to extract quotations from written text. There are
three components in a quotation: source refers to the holder of the quotation,
cue is the trigger word(s), and content is the main body. Existing solutions
for quotation extraction mainly utilize rule-based approaches and sequence
labeling models. While rule-based approaches often lead to low recalls,
sequence labeling models cannot well handle quotations with complicated
structures. In this paper, we propose the Context and Former-Label Enhanced Net
(CofeNet) for quotation extraction. CofeNet is able to extract complicated
quotations with components of variable lengths and complicated structures. On
two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset
(i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art
performance on complicated quotation extraction.Comment: Accepted by COLING 202
Machine translation evaluation resources and methods: a survey
We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT.
This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Event extraction is a fundamental task for natural language processing.
Finding the roles of event arguments like event participants is essential for
event extraction. However, doing so for real-life event descriptions is
challenging because an argument's role often varies in different contexts.
While the relationship and interactions between multiple arguments are useful
for settling the argument roles, such information is largely ignored by
existing approaches. This paper presents a better approach for event extraction
by explicitly utilizing the relationships of event arguments. We achieve this
through a carefully designed task-oriented dialogue system. To model the
argument relation, we employ reinforcement learning and incremental learning to
extract multiple arguments via a multi-turned, iterative process. Our approach
leverages knowledge of the already extracted arguments of the same sentence to
determine the role of arguments that would be difficult to decide individually.
It then uses the newly obtained information to improve the decisions of
previously extracted arguments. This two-way feedback process allows us to
exploit the argument relations to effectively settle argument roles, leading to
better sentence understanding and event extraction. Experimental results show
that our approach consistently outperforms seven state-of-the-art event
extraction methods for the classification of events and argument role and
argument identification
Lexical innovation on the web and social media
This dissertation investigates the emergence and diffusion of English neologisms on the web and social media, employing a data-driven methodology to identify a substantial sample of 851 neologisms. Neologisms are examined from their coining to successful dissemination within the community, with the study revealing a wide spectrum of degrees of diffusion. The exploration extends to studying the usage and diffusion of selected neologisms on the web and on Twitter, with a particular focus on social dynamics and variation among different speaker groups. Moreover, the dissertation probes into semantic innovation, demonstrating substantial socio-semantic variation and polarized public discourse surrounding certain neologisms. The research conducts an extensive analysis of semantic innovation and socio-semantic variation, elucidating significant socio-semantic discrepancies between various communities. The dissertation sheds light on the social and semantic dynamics underpinning the life cycle of neologisms within a linguistically diverse community