Web tables comprise a rich source of factual information. However, without semantic annotation of the tables’ content the information is not usable for automatic integration and search. We propose a methodology to annotate table headers with semantic type information based on the content of column’s cells. In our experiments on 50 tables we achieved an F1 value of 0.55, where the accuracy great...

In this paper, we present a Long Short-TermMemory (LSTM) based model for the task of Chinese Weibo word segmentation. The model adopts a LSTM layer to capture long-range dependencies in sentence and learn the underlying patterns. In order to infer the optimal tag path, we introduce a transition score matrix for jumping between tags of successive characters. Integrated with some unsupervised fea...

In this paper we present methods for improving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the dependency between conjunct lexical heads. Our changes to the baseline model re...

Previous research examining the time course of lexical access during word recognition suggests that phonological processing precedes access to semantic information, which in turn precedes access to syntactic information. Bilingual word recognition likely requires an additional level: knowledge of which language a specific word belongs to. Using the recording of event-related potentials, we inve...

In this paper, we present a plagiarized source retrieval system called CopyCaptor using global word frequency and local feedback to generate an effective query for finding plagiarized source documents from the given suspicious document on PAN’13 source retrieval task. The system achieved 3rd place in competition with 0.33 F1 score, 0.50 precision and 0.33 recall on the test which find appropria...

This paper proposes the adoption of different word-level scores in the framework of automatic pronunciation scoring using learning to rank. Six types of phone-level scores are first computed and converted to word-level scores by using average-based, vowel-based, and consonant-based methods. Different score combination methods are then used to combine these word-level scores to obtain the final ...

INTRODUCTION Acute kidney injury (AKI) risk prediction scores are an objective and transparent means to enable cohort enrichment in clinical trials or to risk stratify patients preoperatively. Existing scores are limited in that they have been designed to predict only severe, or non-consensus AKI definitions and not less severe stages of AKI, which also have prognostic significance. The aim of ...

In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance. Existing methods for text-to-bird/flower synthesis are still far from solving fine-grained image generation problem, due to complex structure heterogeneous appearance that pedestrians naturally take on. To end, propose Multi-Grained Discriminat...

Word sense discrimination is the problem of identifying different contexts that refer to the same meaning of an ambiguous word. For example, given multiple contexts that include the word ’sharp’, we would hope to discriminate between those that refer to an intellectual sharpness versus those that refer to a cutting sharpness. Our methodology is based on the strong contextual hypothesis of Mille...