An Approach for Chinese-Japanese Named Entity Equivalents Extraction Using Inductive Learning and Hanzi-Kanji Mapping Table

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using mono-lingual corpora

Learning Image Deraining Transformer Network with Dynamic Dual Self-Attention

Recently, Transformer-based architecture has been introduced into single image deraining task due to its advantage in modeling non-local information. However, existing approaches tend to integrate global features based on a dense self-attention strategy since it tend to uses all similarities of the tokens between the queries and keys. In fact, this strategy leads to ignoring the most relevant information and inducing blurry effect by the irrelevant representations during the feature aggregation. To this end, this paper proposes an effective image deraining Transformer with dynamic dual self-attention (DDSA), which combines both dense and sparse attention strategies to better facilitate clear image reconstruction. Specifically, we only select the most useful similarity values based on top-k approximate calculation to achieve sparse attention. In addition, we also develop a novel spatial-enhanced feed-forward network (SEFN) to further obtain a more accurate representation for achieving high-quality derained results. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed method.Comment: 6 pages, 5 figure

A Two-Tiered Approach for Organizing Slots in Large, Frame-Structured Knowledge Bases

Slots represent semantic relations and play a major role in frame-based representation systems; they not only act as an "instruction set" for knowledge entry but also support most forms of nterferencing. Thus, the organization of slots merits a systematic study in its own righL A taxonomic approach formalizes the organization of the slots and provides a principled intelpretation for their semantics. We discuss the organization of slots from three different perspectives---relation-element, slot-use, and slot-argument-and propose that all three are useful in providing interpretations for slots. We argue that each individual view, by itself, does not offer sufficient semantics for slot organization. On the other hand, forcing all perspectives together destroys the clarity of a principled taxonomy. Therefore, we propose using two taxonomic views: A first taxonomy for slots is based on the relation-element and slot-use views; this taxonomy is domain-independent and promotes knowledge reuse. A second taxonomy, based on a slot-argument view, is domain- dependent and parallels the nonslot taxonomy