作者: Ballad 時(shí)間: 2025-3-21 21:51
https://doi.org/10.1007/978-3-322-88232-5ce by measuring the relational similarity for relational classification task in which we aim to classify a given word-pair to a specific relation from a predefined set of relations. Linear classifier for ranking the best feature for relational space has been compared with different methods namely, K作者: fastness 時(shí)間: 2025-3-22 03:44 作者: 籠子 時(shí)間: 2025-3-22 06:22
Phrase-Level Grouping for Lexical Gap Resolution in Korean-Vietnamese SMT作者: impale 時(shí)間: 2025-3-22 09:56 作者: entrance 時(shí)間: 2025-3-22 14:06 作者: entrance 時(shí)間: 2025-3-22 21:05 作者: 使腐爛 時(shí)間: 2025-3-22 22:55
https://doi.org/10.1007/978-981-10-8438-6artificial intelligence; automatic speech recognition; computational linguistics; machine translations; 作者: Intellectual 時(shí)間: 2025-3-23 01:52
978-981-10-8437-9Springer Nature Singapore Pte Ltd. 2018作者: adipose-tissue 時(shí)間: 2025-3-23 08:46 作者: brachial-plexus 時(shí)間: 2025-3-23 13:05 作者: Muscularis 時(shí)間: 2025-3-23 17:55
,Kolben-(Verdr?ngungs-)Maschine,n social media and the mental health of 2016 Kumamoto earthquake survivors. We first focus on the users who had experienced an earthquake and track their sentiments before and after the disaster using Twitter as a sensor. Consequently, we found that their emotional polarities switch from nervous dur作者: 性上癮 時(shí)間: 2025-3-23 20:31
Str?mungs- und Kolbenmaschinen im Anlagenbau. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory network (Bi-LSTM) produces a sentence-wide feature repres作者: Adulate 時(shí)間: 2025-3-23 23:04 作者: 簡(jiǎn)略 時(shí)間: 2025-3-24 05:36 作者: 追逐 時(shí)間: 2025-3-24 08:37
Thomas Lege,Olaf Kolditz,W. Zielkeame-based patterns are evaluated against state-of-the-art dependency based syntactic patterns and lexico-syntactic patterns, on three independent datasets that differ in size and construction. The results show that the proposed frame-based patterns significantly improve performance, both in terms of作者: Afflict 時(shí)間: 2025-3-24 13:53
https://doi.org/10.1007/978-3-642-61407-1l networks (RNN), in which a gating mechanism is applied before RNN computation. This allows the proposed model to generate appropriate sentences. The RNN-based generator can be learned from unaligned data by jointly training sentence planning and surface realization to produce natural language resp作者: 國家明智 時(shí)間: 2025-3-24 17:18 作者: Concrete 時(shí)間: 2025-3-24 19:22
https://doi.org/10.1007/978-3-322-88232-5f 2,076 Chinese 4-character words. The purpose for the annotation is to provide affect-linked knowledge to text which can be used in affective computing using NLP techniques. Analysis to the annotated data indicates that valence and arousal fit the classical U-shaped distribution. Most importantly, 作者: Thymus 時(shí)間: 2025-3-25 02:57
Der Luftwiderstand von Geschossen,aracterize specific-domains vocabularies. Translating multiword expressions is a challenge for current Statistical Machine Translation (SMT) systems because corpus-based approaches are effective only when large amounts of parallel corpora are available. However, parallel corpora are only available f作者: 有害處 時(shí)間: 2025-3-25 04:19 作者: 剝皮 時(shí)間: 2025-3-25 10:02 作者: 改變 時(shí)間: 2025-3-25 12:07 作者: CARE 時(shí)間: 2025-3-25 16:45
Klaus-Jürgen Peschges,Steffen Manserstudy, in order to further explore this topic, we present an alternative approach to Khmer POS tagging using Conditional Random Fields (CRFs). Since the features greatly affect the tagging accuracy, we investigate five groups of features and use them with the CRF model. First, we study different con作者: 組成 時(shí)間: 2025-3-25 22:41
Str?mungskupplungen und Str?mungswandlerethods are applied prevalently in practice. These are inconsistent and complicated in some cases, due to unstable phonemic, orthographic, and etymological principles. Consequently, statistical approaches are required for the task. We collect and manually align 7,?658 Khmer name Romanization instance作者: sphincter 時(shí)間: 2025-3-26 02:52 作者: 身心疲憊 時(shí)間: 2025-3-26 07:17
A Deep Neural Architecture for Sentence-Level Sentiment Classification in Twitter Social Networking. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory network (Bi-LSTM) produces a sentence-wide feature repres作者: amputation 時(shí)間: 2025-3-26 09:17
Learning Word Embeddings for Aspect-Based Sentiment Analysisom an unannotated corpus and they are independent from their applications. In this paper we aim to enrich the word vectors by adding more information derived from an application of them which is the aspect based sentiment analysis. We propose a new model using a combination of unsupervised and super作者: 陰險(xiǎn) 時(shí)間: 2025-3-26 12:45 作者: Aerate 時(shí)間: 2025-3-26 17:28 作者: CAND 時(shí)間: 2025-3-26 23:24
Semantic Refinement GRU-Based Neural Language Generation for Spoken Dialogue Systemsl networks (RNN), in which a gating mechanism is applied before RNN computation. This allows the proposed model to generate appropriate sentences. The RNN-based generator can be learned from unaligned data by jointly training sentence planning and surface realization to produce natural language resp作者: 大廳 時(shí)間: 2025-3-27 01:58
Discovering Representative Space for Relational Similarity Measurementional similarity is important for various natural language processing tasks such as, relational search, noun-modifier classification, and analogy detection. Despite this need, the features that accurately express the relational similarity between two word pairs remain largely unknown. So far, method作者: insurgent 時(shí)間: 2025-3-27 08:43 作者: sacrum 時(shí)間: 2025-3-27 13:20
Integrating Specialized Bilingual Lexicons of Multiword Expressions for Domain Adaptation in Statistaracterize specific-domains vocabularies. Translating multiword expressions is a challenge for current Statistical Machine Translation (SMT) systems because corpus-based approaches are effective only when large amounts of parallel corpora are available. However, parallel corpora are only available f作者: Lacerate 時(shí)間: 2025-3-27 14:55
Logical Parsing from Natural Language Based on a Neural Translation Modelemantic parser rely on high-quality lexicons, hand-crafted grammars and linguistic features which are limited by applied domain or representation. In this paper, we propose an approach to learn from denotations based on the Seq2Seq model augmented with attention mechanism. We encode input sequence i作者: 向下 時(shí)間: 2025-3-27 17:47 作者: 憤慨一下 時(shí)間: 2025-3-27 22:10 作者: NUL 時(shí)間: 2025-3-28 04:15
Khmer POS Tagging Using Conditional Random Fieldsstudy, in order to further explore this topic, we present an alternative approach to Khmer POS tagging using Conditional Random Fields (CRFs). Since the features greatly affect the tagging accuracy, we investigate five groups of features and use them with the CRF model. First, we study different con作者: gusher 時(shí)間: 2025-3-28 07:14
Statistical Khmer Name Romanizationethods are applied prevalently in practice. These are inconsistent and complicated in some cases, due to unstable phonemic, orthographic, and etymological principles. Consequently, statistical approaches are required for the task. We collect and manually align 7,?658 Khmer name Romanization instance作者: 是比賽 時(shí)間: 2025-3-28 13:34 作者: 誘拐 時(shí)間: 2025-3-28 18:28 作者: Ccu106 時(shí)間: 2025-3-28 22:34
Str?mungskupplungen und Str?mungswandlerndom fields and support vector machine supervised by the manual alignment achieve a precision of .99 on grapheme level, which outperforms a state-of-the-art recurrent neural network approach in a pure sequence-to-sequence manner. The manually aligned data have been released under a license of . for the research community.作者: BIPED 時(shí)間: 2025-3-29 01:09 作者: 歡笑 時(shí)間: 2025-3-29 03:49 作者: START 時(shí)間: 2025-3-29 07:13
Statistical Khmer Name Romanizationndom fields and support vector machine supervised by the manual alignment achieve a precision of .99 on grapheme level, which outperforms a state-of-the-art recurrent neural network approach in a pure sequence-to-sequence manner. The manually aligned data have been released under a license of . for the research community.作者: NORM 時(shí)間: 2025-3-29 12:56 作者: 或者發(fā)神韻 時(shí)間: 2025-3-29 16:17 作者: 外形 時(shí)間: 2025-3-29 21:28 作者: intercede 時(shí)間: 2025-3-30 01:59 作者: 譏笑 時(shí)間: 2025-3-30 07:59 作者: 孤僻 時(shí)間: 2025-3-30 09:58 作者: 吹牛大王 時(shí)間: 2025-3-30 13:26
Thomas Lege,Olaf Kolditz,W. Zielkeddings), and the aspect category and aspect sentiment from labeled and unlabeled data. We conduct experiments on the restaurant review data (.). Experimental results show that our proposed model outperforms other methods as Word2Vec and GloVe.作者: 遺棄 時(shí)間: 2025-3-30 20:12
https://doi.org/10.1007/978-3-322-88232-5which implies that the affective meaning of 4-character words may not be compositional to its component words. The study on this annotated list of 4-character words not only has significance at the intersection of cognitive neuroscience and social psychology, but also has great value as a resource for affective analysis in NLP applications.作者: commodity 時(shí)間: 2025-3-30 22:02
https://doi.org/10.1007/978-3-322-87219-7 language pairs: Indonesian-Vietnamese, Malay-Vietnamese, and Filipino-Vietnamese. By integrating grammatical and morphological information, the proposed method achieved a significant improvement of 0.5 BLEU points. This showed the effectiveness of integrating grammatical and morphological features to pivot translation.作者: opportune 時(shí)間: 2025-3-31 04:53 作者: 毛細(xì)血管 時(shí)間: 2025-3-31 05:04
