Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts.However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lackof sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient trainingdata. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well as theirproximities for transfer learning. Specifically, we construct a co-occurrence graph from texts, and capture both first-order andsecond-order entity proximities for embedding learning. Based on this, we further optimize the distance from entity pairs tocorresponding prototypes, which can be easily adapted to almost arbitrary RE frameworks. Thus, the learning of infrequent or evenunseen relation types will benefit from semantically proximate relations through pairs of entities and large-scale textual information.We have conducted extensive experiments on two publicly available datasets: New York Times and Google Distant Supervision.Compared with eight state-of-the-art baselines, our proposed model achieves significant improvements (4.1% F1 on average). Furtherresults on long-tail relations demonstrate the effectiveness of the learned relation prototypes. We further conduct an ablation study toinvestigate the impacts of varying components, and apply it to four basic relation extraction models to verify the generalization ability.Finally, we analyze several example cases to give intuitive impressions as qualitative analysis. Our codes will be released later

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research

Neural approaches to discourse coherence: modeling, evaluation and application

Discourse coherence is an important aspect of text quality that refers to the way different textual units relate to each other. In this thesis, I investigate neural approaches to modeling discourse coherence. I present a multi-task neural network where the main task is to predict a document-level coherence score and the secondary task is to learn word-level syntactic features. Additionally, I examine the effect of using contextualised word representations in single-task and multi-task setups. I evaluate my models on a synthetic dataset where incoherent documents are created by shuffling the sentence order in coherent original documents. The results show the efficacy of my multi-task learning approach, particularly when enhanced with contextualised embeddings, achieving new state-of-the-art results in ranking the coherent documents higher than the incoherent ones (96.9%). Furthermore, I apply my approach to the realistic domain of people’s everyday writing, such as emails and online posts, and further demonstrate its ability to capture various degrees of coherence. In order to further investigate the linguistic properties captured by coherence models, I create two datasets that exhibit syntactic and semantic alterations. Evaluating different models on these datasets reveals their ability to capture syntactic perturbations but their inadequacy to detect semantic changes. I find that semantic alterations are instead captured by models that first build sentence representations from averaged word embeddings, then apply a set of linear transformations over input sentence pairs. Finally, I present an application for coherence models in the pedagogical domain. I first demonstrate that state of-the-art neural approaches to automated essay scoring (AES) are not robust to adversarially created, grammatical, but incoherent sequences of sentences. Accordingly, I propose a framework for integrating and jointly training a coherence model with a state-of-the-art neural AES system in order to enhance its ability to detect such adversarial input. I show that this joint framework maintains a performance comparable to the state-of-the-art AES system in predicting a holistic essay score while significantly outperforming it in adversarial detection

HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.Comment: 9 pages, 2 figure

Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token level or span level) and have some weaknesses of the corresponding granularity. In this paper, we first unify token and span level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token-level and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets.Comment: Accepted by ACM Transactions on Information System