共查询到20条相似文献，搜索用时 46 毫秒
1.
Opinion mining in a multilingual and multi-domain environment as YouTube requires models to be robust across domains as well as languages, and not to rely on linguistic resources (e.g. syntactic parsers, POS-taggers, pre-defined dictionaries) which are not always available in many languages. In this work, we i) proposed a convolutional N-gram BiLSTM (CoNBiLSTM) word embedding which represents a word with semantic and contextual information in short and long distance periods; ii) applied CoNBiLSTM word embedding for predicting the type of a comment, its polarity sentiment (positive, neutral or negative) and whether the sentiment is directed toward the product or video; iii) evaluated the efficiency of our model on the SenTube dataset, which contains comments from two domains (i.e. automobile, tablet) and two languages (i.e. English, Italian). According to the experimental results, CoNBiLSTM generally outperforms the approach using SVM with shallow syntactic structures (STRUCT) – the current state-of-the-art sentiment analysis on the SenTube dataset. In addition, our model achieves more robustness across domains than the STRUCT (e.g. 7.47% of the difference in performance between the two domains for our model vs. 18.8% for the STRUCT) 相似文献
2.
Frank Z. Xing Filippo Pallucchini Erik Cambria 《Information processing & management》2019,56(3):554-564
Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains. 相似文献
3.
Preprocessing is one of the key components in a typical text classification framework. This paper aims to extensively examine the impact of preprocessing on text classification in terms of various aspects such as classification accuracy, text domain, text language, and dimension reduction. For this purpose, all possible combinations of widely used preprocessing tasks are comparatively evaluated on two different domains, namely e-mail and news, and in two different languages, namely Turkish and English. In this way, contribution of the preprocessing tasks to classification success at various feature dimensions, possible interactions among these tasks, and also dependency of these tasks to the respective languages and domains are comprehensively assessed. Experimental analysis on benchmark datasets reveals that choosing appropriate combinations of preprocessing tasks, rather than enabling or disabling them all, may provide significant improvement on classification accuracy depending on the domain and language studied on. 相似文献
4.
Cross-lingual semantic interoperability has drawn significant attention in recent digital library and World Wide Web research as the information in languages other than English has grown exponentially. Cross-lingual information retrieval (CLIR) across different European languages, such as English, Spanish, and French, has been widely explored; however, CLIR across European languages and Oriental languages is still in the initial stage. To cross language boundary, corpus-based approach is promising to overcome the limitation of the knowledge-based and controlled vocabulary approaches but collecting parallel corpora between European language and Oriental language is not an easy task. Length-based and text-based approaches are two major approaches to align parallel documents. In this paper, we investigate several techniques using these approaches and compare their performances in aligning English and Chinese titles of parallel documents available on the Web. 相似文献
5.
Salud María Jiménez-Zafra M. Teresa Martín-Valdivia M. Dolores Molina-González L. Alfonso Ureña-López 《Information processing & management》2018,54(2):240-251
Up to now, negation is a challenging problem in the context of Sentiment Analysis. The study of negation implies the correct identification of negation markers, the scope and the interpretation of how negation affects the words that are within it, that is, whether it modifies their meaning or not and if so, whether it reverses, reduces or increments their polarity value. In addition, if we are interested in managing reviews in languages other than English, the issue becomes even more problematic due to the lack of resources. The present work shows the validity of the SFU ReviewSP-NEG corpus, which we annotated at negation level, for training supervised polarity classification systems in Spanish. The assessment has involved the comparison of different supervised models. The results achieved show the validity of the corpus and allow us to state that the annotation of how negation affects the words that are within its scope is important. Therefore, we propose to add a new phase to tackle negation in polarity classification systems (phase iii): i) identification of negation cues, ii) determination of the scope of negation, iii) identification of how negation affects the words that are within its scope, and iv) polarity classification taking into account negation. 相似文献
6.
A challenge for sentence categorization and novelty mining is to detect not only when text is relevant to the user’s information need, but also when it contains something new which the user has not seen before. It involves two tasks that need to be solved. The first is identifying relevant sentences (categorization) and the second is identifying new information from those relevant sentences (novelty mining). Many previous studies of relevant sentence retrieval and novelty mining have been conducted on the English language, but few papers have addressed the problem of multilingual sentence categorization and novelty mining. This is an important issue in global business environments, where mining knowledge from text in a single language is not sufficient. In this paper, we perform the first task by categorizing Malay and Chinese sentences, then comparing their performances with that of English. Thereafter, we conduct novelty mining to identify the sentences with new information. Experimental results on TREC 2004 Novelty Track data show similar categorization performance on Malay and English sentences, which greatly outperform Chinese. In the second task, it is observed that we can achieve similar novelty mining results for all three languages, which indicates that our algorithm is suitable for novelty mining of multilingual sentences. In addition, after benchmarking our results with novelty mining without categorization, it is learnt that categorization is necessary for the successful performance of novelty mining. 相似文献
7.
English is the main link language across cultures today.The native English speakers benefit most from the English hegemonism for they share the English centered information flows and the recognitions of the scientific achievements.English native users may be more competitive in academic fields and other related industries(such as publishing industry)because of the language they speak.And the problem of endangered languages is essentially due to the worldwide spread and hegemony of English in the world.The worldwide use of English has destroyed the linguistic diversity of the world. 相似文献
8.
Qing Li Yuanzhu Peter Chen Sung-Hyon Myaeng Yun Jin Bo-Yeong Kang 《Information processing & management》2009
For historical and cultural reasons, English phases, especially proper nouns and new words, frequently appear in Web pages written primarily in East Asian languages such as Chinese, Korean, and Japanese. Although such English terms and their equivalences in these East Asian languages refer to the same concept, they are often erroneously treated as independent index units in traditional Information Retrieval (IR). This paper describes the degree to which the problem arises in IR and proposes a novel technique to solve it. Our method first extracts English terms from native Web documents in an East Asian language, and then unifies the extracted terms and their equivalences in the native language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to achieving retrieval performance at the level of Mono-Lingual Information Retrieval (MLIR) is the translation of terms in search queries which can not be found in a bilingual dictionary. The Web mining approach proposed in this paper for concept unification of terms in different languages can also be applied to solve this well-known challenge in CLIR. Experimental results based on NTCIR and KT-Set test collections show that the high translation precision of our approach greatly improves performance of both Mono-Lingual and Cross-Language Information Retrieval. 相似文献
9.
以韩文著者号资料搜索为例,说明小语种编目工作所需的3种能力,即编目知识、外语能力、互联网应用技能。认为外语能力包括英语能力及小语种能力;在英语已经成为国际通用语言的情况下,很多小语种资料都可能会有英文网页,因此可用英语作为中间语种。在互联网搜索到相关知识。 相似文献
10.
部分初中学生用汉语的思维来理解和学习英语,对英汉两种语言差异了解不够,对系动词和实义动词的区别和用法混沌不清,从而导致他们容易犯系动词和实义动词不当混用的错误,这影响了他们英语学习成绩的提高,不利于其英语思维的培养。基于此,本文对此类错误进行分析,使学生加深对英汉两种语言的差异和相关英语语法规则的了解,按照英语的语言规则做出正确的表达。教师可以通过对语法的讲解和反复的练习来提升学生的语言规则意识,调动学生的积极情感,提高学生的认知能力,促使学生英语的语言习惯和能力在自然过程中形成。 相似文献
11.
《Information processing & management》2022,59(6):103065
Named entity recognition aims to detect pre-determined entity types in unstructured text. There is a limited number of studies on this task for low-resource languages such as Turkish. We provide a comprehensive study for Turkish named entity recognition by comparing the performances of existing state-of-the-art models on the datasets with varying domains to understand their generalization capability and further analyze why such models fail or succeed in this task. Our experimental results, supported by statistical tests, show that the highest weighted F1 scores are obtained by Transformer-based language models, varying from 80.8% in tweets to 96.1% in news articles. We find that Transformer-based language models are more robust to entity types with a small sample size and longer named entities compared to traditional models, yet all models have poor performance for longer named entities in social media. Moreover, when we shuffle 80% of words in a sentence to imitate flexible word order in Turkish, we observe more performance deterioration, 12% in well-written texts, compared to 7% in noisy text. 相似文献
12.
One of the most important opinion mining research directions falls in the extraction of polarities referring to specific entities (aspects) contained in the analyzed texts. The detection of such aspects may be very critical especially when documents come from unknown domains. Indeed, while in some contexts it is possible to train domain-specific models for improving the effectiveness of aspects extraction algorithms, in others the most suitable solution is to apply unsupervised techniques by making such algorithms domain-independent and more efficient in a real-time environment. Moreover, an emerging need is to exploit the results of aspect-based analysis for triggering actions based on these data. This led to the necessity of providing solutions supporting both an effective analysis of user-generated content and an efficient and intuitive way of visualizing collected data. In this work, we implemented an opinion monitoring service implementing (i) a set of unsupervised strategies for aspect-based opinion mining together with (ii) a monitoring tool supporting users in visualizing analyzed data. The aspect extraction strategies are based on the use of an open information extraction strategy. The effectiveness of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domain-adapted techniques. 相似文献
13.
[目的/意义]针对网络产品评论细粒度意见挖掘的研究进展进行分析和总结,在明确其主要任务的基础上,探讨涉及的关键技术、研究成果以及未来发展趋势,为该领域研究未来的发展提供建议。[方法/过程]本文主要采用文献综述的方法,对国内外相关研究进展进行分析和归纳,由粗粒度意见挖掘引申到细粒度意见挖掘,在明确细粒度意见挖掘主要任务的基础上,重点针对其关键技术和研究进展进行总结。[结果/结论]本文明确了网络产品评论细粒度意见挖掘的主要任务,包括主客观句分类、评价要素抽取和情感极性计算,总结了各个任务涉及的关键技术。 相似文献
14.
由于地理、历史、宗教信仰、生活习惯等方面的差异,英汉语习语承载着不少民族文化特色和文化信息。习语中的文化因素往往是翻译中的难点。文章试图对英汉习语的文化差异和翻译作一些探索。 相似文献
15.
A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection
Endang Wahyu Pamungkas Valerio Basile Viviana Patti 《Information processing & management》2021,58(4):102544
Hate speech is an increasingly important societal issue in the era of digital communication. Hateful expressions often make use of figurative language and, although they represent, in some sense, the dark side of language, they are also often prime examples of creative use of language. While hate speech is a global phenomenon, current studies on automatic hate speech detection are typically framed in a monolingual setting. In this work, we explore hate speech detection in low-resource languages by transferring knowledge from a resource-rich language, English, in a zero-shot learning fashion. We experiment with traditional and recent neural architectures, and propose two joint-learning models, using different multilingual language representations to transfer knowledge between pairs of languages. We also evaluate the impact of additional knowledge in our experiment, by incorporating information from a multilingual lexicon of abusive words. The results show that our joint-learning models achieve the best performance on most languages. However, a simple approach that uses machine translation and a pre-trained English language model achieves a robust performance. In contrast, Multilingual BERT fails to obtain a good performance in cross-lingual hate speech detection. We also experimentally found that the external knowledge from a multilingual abusive lexicon is able to improve the models’ performance, specifically in detecting the positive class. The results of our experimental evaluation highlight a number of challenges and issues in this particular task. One of the main challenges is related to the issue of current benchmarks for hate speech detection, in particular how bias related to the topical focus in the datasets influences the classification performance. The insufficient ability of current multilingual language models to transfer knowledge between languages in the specific hate speech detection task also remain an open problem. However, our experimental evaluation and our qualitative analysis show how the explicit integration of linguistic knowledge from a structured abusive language lexicon helps to alleviate this issue. 相似文献
16.
《Information processing & management》2019,56(2):320-342
Sentiment analysis (SA) is a continuing field of research that lies at the intersection of many fields such as data mining, natural language processing and machine learning. It is concerned with the automatic extraction of opinions conveyed in a certain text. Due to its vast applications, many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention. This survey presents a comprehensive overview of the works done so far on Arabic SA (ASA). The survey groups published papers based on the SA-related problems they address and tries to identify the gaps in the current literature laying foundation for future studies in this field. 相似文献
17.
《Information processing & management》2023,60(3):103261
Cross-genre author profiling aims to build generalized models for predicting profile traits of authors that can be helpful across different text genres for computer forensics, marketing, and other applications. The cross-genre author profiling task becomes challenging when dealing with low-resourced languages due to the lack of availability of standard corpora and methods. The task becomes even more challenging when the data is code-switched, which is informal and unstructured. In previous studies, the problem of cross-genre author profiling has been mainly explored for mono-lingual texts in highly resourced languages (English, Spanish, etc.). However, it has not been thoroughly explored for the code-switched text which is widely used for communication over social media. To fulfill this gap, we propose a transfer learning-based solution for the cross-genre author profiling task on code-switched (English–RomanUrdu) text using three widely known genres, Facebook comments/posts, Tweets, and SMS messages. In this article, firstly, we experimented with the traditional machine learning, deep learning and pre-trained transfer learning models (MBERT, XLMRoBERTa, ULMFiT, and XLNET) for the same-genre and cross-genre gender identification task. We then propose a novel Trans-Switch approach that focuses on the code-switching nature of the text and trains on specialized language models. In addition, we developed three RomanUrdu to English translated corpora to study the impact of translation on author profiling tasks. The results show that the proposed Trans-Switch model outperforms the baseline deep learning and pre-trained transfer learning models for cross-genre author profiling task on code-switched text. Further, the experimentation also shows that the translation of RomanUrdu text does not improve results. 相似文献
18.
Knowledge acquisition and bilingual terminology extraction from multilingual corpora are challenging tasks for cross-language information retrieval. In this study, we propose a novel method for mining high quality translation knowledge from our constructed Persian–English comparable corpus, University of Tehran Persian–English Comparable Corpus (UTPECC). We extract translation knowledge based on Term Association Network (TAN) constructed from term co-occurrences in same language as well as term associations in different languages. We further propose a post-processing step to do term translation validity check by detecting the mistranslated terms as outliers. Evaluation results on two different data sets show that translating queries using UTPECC and using the proposed methods significantly outperform simple dictionary-based methods. Moreover, the experimental results show that our methods are especially effective in translating Out-Of-Vocabulary terms and also expanding query words based on their associated terms. 相似文献
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20.
在国际交流交往日益频繁的现代社会,外语成为必备的交流工具,多掌握一门外语就多了一门技能,就会在未来的工作、学习中更占据优势。特别对于英语专业学生,为了能够在激烈的市场竞争中获得优势地位,就要拥有更多的本领和技能,更多英语专业的大学生已经开始认识到学习和掌握第二门外语的重要性。然而,如何轻松、有效地学好第二门语言,是值得探究的话题。本文以二外法语作为探究对象,分析了高校英语专业二外法语的教学。 相似文献