A novel approach to melt purification of magnesium alloys

AbstractA novel low-cost method for melt purification of magnesium alloys, the melt self-purifying technology (MSPT), has been developed successfully based on a low temperature melt treatment (LTMT) without adding any fluxes. The iron solubility in the molten liquid of magnesium and its alloys, and the settlement velocity of iron particles were calculated. It is shown that the low temperature melt treatment is an effective method to decrease the impurity Fe content in magnesium and its alloys. Without any additions, the Fe content in the AZ31 alloy was reduced to 15 ppm from the initial 65 ppm, and the Fe content in the AZ61 melt was decreased to 20 ppm from the initial 150 ppm after the low temperature melt treatment. The results also showed that the Fe content in AM60 and AM50 dropped to 15 and 18 ppm, respectively, from the initial 150 ppm after the low temperature melt treatment. For ZK 60, the Fe content in the melt down to less than 5 ppm was achieved. After the low temperature melt treatment, the Si content in the above alloys was also decreased obviously

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.Comment: accepted at CIKM 201

Chinese Word Segmentation Using Minimal Linguistic Knowledge

This paper presents a primarily data-driven Chinese word segmentation system and its performances on the closed track using two corpora at the first international Chinese word segmentation bakeoff. The system consists of a new words recognizer, a base segmentation algorithm, and procedures for combining single characters, suffixes, and checking segmentation consistencies

Translation Term Weighting and Combining Translation Resources in

Introduction In TREC-10 the Berkeley group participated only in the English-Arabic cross-language retrieval (CLIR) track. One Arabic monolingual run and four English-Arabic cross-language runs were submitted. Our approach to the cross-language retrieval was to translate the English topics into Arabic using online EnglishArabic bilingual dictionaries and machine translation software. The five official runs are named as BKYAAA1, BKYEAA1, BKYEAA2, BKYEAA3, and BKYEAA4. The BKYAAA1 is the Arabic monolingual run, and the rest are English-to-Arabic cross-language runs. The same logistic regression based document ranking algorithm without pseudo relevance feedback was applied in all five runs. We refer the readers to the paper in [1] for details. 2 Test Collection The document collection used in TREC-10 cross-language track consists of 383,872 Arabic articles from the Agence France Press (AFP) Arabic Newswire during the period from 13 May, 1994 to 20 December, 2000. There are 25 English

Experiments on Cross-language and Patent Retrieval at NTCIR-3 Workshop

The Berkeley group participated in the crosslanguage retrieval task and the patent retrieval task at the third NTCIR workshop. This paper describes our experiments on cross-language and patent retrieval. We present an automatic relevance feedback procedure for document ranking formula based on logistic regression, and a procedure for automatically extracting Chinese/Japanese translations of English words from search results returned from Internet search engines using English words as queries

1 Summary Building an Arabic Stemmer for Information Retrieval

In TREC 2002 the Berkeley group participated only in the English-Arabic cross-language retrieval (CLIR) track. One Arabic monolingual run and three English-Arabic cross-language runs were submitted. Our approach to the crosslanguage retrieval was to translate the English topics into Arabic using online English-Arabic machine translation systems. The four official runs are named as BKYMON, BKYCL1, BKYCL2, and BKYCL3. The BKYMON is the Arabic monolingual run, and the other three runs are English-to-Arabic cross-language runs. This paper reports on the construction of an Arabic stoplist and two Arabic stemmers, and the experiments on Arabic monolingual retrieval, English-to-Arabic cross-language retrieval.