SAMAR: Subjectivity and sentiment analysis for Arabic social media

SAMAR is a system for subjectivity and sentiment analysis (SSA) for Arabic social media genres. Arabic is a morphologically rich language, which presents significant complexities for standard approaches to building SSA systems designed for the English language. Apart from the difficulties presented by the social media genres processing, the Arabic language inherently has a high number of variable word forms leading to data sparsity. In this context, we address the following 4 pertinent issues: how to best represent lexical information; whether standard features used for English are useful for Arabic; how to handle Arabic dialects; and, whether genre specific features have a measurable impact on performance. Our results show that using either lemma or lexeme information is helpful, as well as using the two part of speech tagsets (RTS and ERTS). However, the results show that we need individualized solutions for each genre and task, but that lemmatization and the ERTS POS tagset are present in a majority of the settings.     

中文文本体裁的自动分类机制

文本按体裁自动分类属于按文本的形式分类的范畴,所以它与按内容自动分类问题有许多的不同之处,本文提出了一种关于中文文本体裁自动分类的新机制。在体裁分类过程中首要的问题是分类特征的选取,体裁分类特征项分为两种方式加以描述,一是集合形式,如基于分类词典和语料统计的政论性词汇和情感词汇等,二是规则形式,如公文标识信息和条文句等。基于根据特征之间的关联性和差异性,采用样本分布决策的方法抽取相应的特征项。最后利用支撑向量机算法进行自动分类。该机制已经在五类体裁的语料上得到实现,并获得了较好的效果。     

The WaCky wide web: a collection of very large linguistically processed web-crawled corpora

This article introduces ukWaC, deWaC and itWaC, three very large corpora of English, German, and Italian built by web crawling, and describes the methodology and tools used in their construction. The corpora contain more than a billion words each, and are thus among the largest resources for the respective languages. The paper also provides an evaluation of their suitability for linguistic research, focusing on ukWaC and itWaC. A comparison in terms of lexical coverage with existing resources for the languages of interest produces encouraging results. Qualitative evaluation of ukWaC versus the British National Corpus was also conducted, so as to highlight differences in corpus composition (text types and subject matters). The article concludes with practical information about format and availability of corpora and tools.     

汉语语体的计量特征在文本聚类中的应用

提出了将语言计量研究成果应用于文本聚类研究的方法。通过两个50万词的语料样本发现了在现代汉语口语体和书面语体中具有显著分布差异的16个语言结构特征;以其中7个作为文本表示特征准确地将实验文本聚类为口语体（相似度89.84%）和书面语体（相似度86.93%）两类。以语言结构的计量特征表示文本的方法加强了聚类/分类研究的可解释性,具有较高的理论和应用价值。以语料库和统计方法进行语体特征计量研究是汉语语体描写研究的重要方法,阐述了其理论基础。     

RedTweet: recommendation engine for reddit

Twitter and Reddit are two of the most popular social media sites used today. In this paper, we study the use of machine learning and WordNet-based classifiers to generate an interest profile from a user’s tweets and use this to recommend loosely related Reddit threads which the reader is most likely to be interested in. We introduce a genre classification algorithm using a similarity measure derived from WordNet lexical database for English to label genres for nouns in tweets. The proposed algorithm generates a user’s interest profile from their tweets based on a referencing taxonomy of genres derived from the genre-tagged Brown Corpus augmented with a technology genre. The top K genres of a user’s interest profile can be used for recommending subreddit articles in those genres. Experiments using real life test cases collected from Twitter have been done to compare the performance on genre classification by using the WordNet classifier and machine learning classifiers such as SVM, Random Forests, and an ensemble of Bayesian classifiers. Empirically, we have obtained similar results from the two different approaches with a sufficient number of tweets. It seems that machine learning algorithms as well as the WordNet ontology are viable tools for developing recommendation engine based on genre classification. One advantage of the WordNet approach is simplicity and no learning is required. However, the WordNet classifier tends to have poor precision on users with very few tweets.     

Investigating the contribution of linguistic information to quality estimation

This paper describes a study on the contribution of linguistically-informed features to the task of quality estimation for machine translation at sentence level. A standard regression algorithm is used to build models using a combination of linguistic and non-linguistic features extracted from the input text and its machine translation. Experiments with three English–Spanish translation datasets show that linguistic features on their own are not able to outperform shallower features based on statistics from the input text, its translation and additional corpora. However, further analysis suggests that linguistic information can be useful to produce better results if carefully combined with other features. An in-depth analysis of the results highlights a number of issues related to the use of linguistic features.     

A Multilingual Text Mining Approach Based on Self-Organizing Maps

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge from multilingual information sources. Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. However, currently most text mining tools mainly focus only on processing monolingual documents (particularly English documents): little attention has been paid to apply the techniques to handle the documents in Asian languages, and further extend the mining algorithms to support the aspects of multilingual information sources. In this work, we attempt to develop a language-neutral method to tackle the linguistics difficulties in the text mining process. Using a variation of automatic clustering techniques, which apply a neural net approach, namely the Self-Organizing Maps (SOM), we have conducted several experiments to uncover associated documents based on a Chinese corpus, Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. The experiments show some interesting results and a couple of potential paths for future work in the field of multilingual information discovery. Besides, this work is expected to act as a starting point for exploring the impacts on linguistics issues with the machine-learning approach to mining sensible linguistics elements from multilingual text collections.     

The potential of Swetra — A multilanguage MT system

One may indicate the potentials of an MT system by stating what text genres it can process, e.g., weather reports and technical manuals. This approach is practical, but misleading, unless domain knowledge is highly integrated in the system. Another way to indicate which fragments of language the system can process is to state its grammatical potentials, or more formally, which languages the grammars of the system can generate. This approach is more technical and less understandable to the layman (customer), but it is less misleading, since it stresses the point that the fragments which can be translated by the grammars of a system need not necessarily coincide exactly with any particular genre. Generally, the syntactic and lexical rules of an MT system allow it to translate many sentences other than those belonging to a certain genre. On the other hand it probably cannot translate all the sentences of a particular genre. Swetra is a multilanguage MT system defined by the potentials of a formal grammar (standard referent grammar) and not by reference to a genre. Successful translation of sentences can be guaranteed if they are within a specified syntactic format based on a specified lexicon. The paper discusses the consequences of this approach (Grammatically Restricted Machine Translation, GRMT) and describes the limits set by a standard choice of grammatical rules for sentences and clauses, noun phrases, verb phrases, sentence adverbials, etc. Such rules have been set up for English, Swedish and Russian, mainly on the basis of familiarity (frequency) and computer efficiency, but restricting the grammar and making it suitable for several languages poses many problems for optimization. Sample texts — newspaper reports — illustrate the type of text that can be translated with reasonable success among Russian, English and Swedish.     

Mining language variation using word using and collocation characteristics

Two textual metrics “Frequency Rank” (FR) and “Intimacy” are proposed in this paper to measure the word using and collocation characteristics which are two important aspects of text style. The FR, derived from the local index numbers of terms in a sentences ordered by the global frequency of terms, provides single-term-level information. The Intimacy models relationship between a word and others, i.e. the closeness a term is to other terms in the same sentence. Two textual features “Frequency Rank Ratio (FRR)” and “Overall Intimacy (OI)” for capturing language variation are derived by employing the two proposed textual metrics. Using the derived features, language variation among documents can be visualized in a text space. Three corpora consisting of documents of diverse topics, genres, regions, and dates of writing are designed and collected to evaluate the proposed algorithms. Extensive simulations are conducted to verify the feasibility and performance of our implementation. Both theoretical analyses based on entropy and the simulations demonstrate the feasibility of our method. We also show the proposed algorithm can be used for visualizing the closeness of several western languages. Variation of modern English over time is also recognizable when using our analysis method. Finally, our method is compared to conventional text classification implementations. The comparative results indicate our method outperforms the others.     

The potential of swetra—A multilanguage MT system

One may indicate the potentials of an MT system by stating what text genres it can process, e.g., weather reports and technical manuals. This approach is practical, but misleading, unless domain knowledge is highly integrated in the system. Another way to indicate which fragments of language the system can process is to state its grammatical potentials, or more formally, which languages the grammars of the system can generate. This approach is more technical and less understandable to the layman (customer), but it is less misleading, since it stresses the point that the fragments which can be translated by the grammars of a system need not necessarily coincide exactly with any particular genre. Generally, the syntactic and lexical rules of an MT system allow it to translate many sentences other than those belonging to a certain genre. On the other hand it probably cannot translate all the sentences of a particular genre. Swetra is a multilanguage MT system defined by the potentials of a formal grammar (standard referent grammar) and not by reference to a genre. Successful translation of sentences can be guaranteed if they are within a specified syntactic format based on a specified lexicon. The paper discusses the consequences of this approach (Grammatically Restricted Machine Translation, GRMT) and describes the limits set by a standard choice of grammatical rules for sentences and clauses, noun phrases, verb phrases, sentence adverbials, etc. Such rules have been set up for English, Swedish and Russian, mainly on the basis of familiarity (frequency) and computer efficiency, but restricting the grammar and making it suitable for several languages poses many problems for optimization. Sample texts—newspaper reports—illustrate the type of text that can be translated with reasonable success among Russian, English and Swedish.     

Comparative evaluation of tools for Arabic corpora search and analysis

As the number of Arabic corpora is constantly increasing, there is an obvious and growing need for concordancing software for corpus search and analysis that supports as many features as possible of the Arabic language, and provides users with a greater number of functions. This paper evaluates six existing corpus search and analysis tools based on eight criteria which seem to be the most essential for searching and analysing Arabic corpora, such as displaying Arabic text in its right-to-left direction, normalising diacritics and Hamza, and providing an Arabic user interface. The results of the evaluation revealed that three tools: Khawas, Sketch Engine, and aConCorde, have met most of the evaluation criteria and achieved the highest benchmark scores. The paper concluded that developers’ conscious consideration of the linguistic features of Arabic when designing these three tools was the most significant factor behind their superiority.     

A corpora-based detection of stylistic inconsistencies of text in the targeted subgenre

Any text, be it written by humans or generated by computers, must choose a specific style and be consistent. Textual style is based on the genre to which the text should belong. Each genre has its unique characteristics based on the style of narration, the topic, the target audience, and the author’s intentions. Thus, in order to write or produce better passages, a finer-grained style checker sensitive to differences among genres is to be developed. We propose a foundational method for this purpose: the flexible accumulation of data of the style features of atomic expressions based on any kind of contrastive sets of texts representing the good and bad examples for the targeted genre, an analysis of a given text using the style features, and a visualization to effectively help the author detect anomalies for the targeted genre. Any type of genre-sensitive style checker will implement our method or similar ones.     

Finding Syntactic Structure in Unparsed Corpora The Gsearch Corpus Query System

The Gsearch system allows the selection of sentences by syntacticcriteria from text corpora, even when these corpora contain no priorsyntactic markup. This is achieved by means of a fast chart parser,which takes as input a grammar and a search expression specified by theuser. Gsearch features a modular architecture that can be extendedstraightforwardly to give access to new corpora. The Gsearcharchitecture also allows interfacing with external linguistic resources(such as taggers and lexical databases). Gsearch can be used withgraphical tools for visualizing the results of a query.     

METIS-II: low resource machine translation

METIS-II was an EU-FET MT project running from October 2004 to September 2007, which aimed at translating free text input without resorting to parallel corpora. The idea was to use “basic” linguistic tools and representations and to link them with patterns and statistics from the monolingual target-language corpus. The METIS-II project has four partners, translating from their “home” languages Greek, Dutch, German, and Spanish into English. The paper outlines the basic ideas of the project, their implementation, the resources used, and the results obtained. It also gives examples of how METIS-II has continued beyond its lifetime and the original scope of the project. On the basis of the results and experiences obtained, we believe that the approach is promising and offers the potential for development in various directions.     

Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

Named Entity Recognition and Classification (NERC) is an important component of applications like Opinion Tracking, Information Extraction, or Question Answering. When these applications require to work in several languages, NERC becomes a bottleneck because its development requires language-specific tools and resources like lists of names or annotated corpora. This paper presents a lightly supervised system that acquires lists of names and linguistic patterns from large raw text collections in western languages and starting with only a few seeds per class selected by a human expert. Experiments have been carried out with English and Spanish news collections and with the Spanish Wikipedia. Evaluation of NE classification on standard datasets shows that NE lists achieve high precision and reveals that contextual patterns increase recall significantly. Therefore, it would be helpful for applications where annotated NERC data are not available such as those that have to deal with several western languages or information from different domains.     

Using EmotiBlog to annotate and analyse subjectivity in the new textual genres

Thanks to the increasing amount of subjective data on the Web 2.0, tools to manage and exploit such data become essential. Our research is focused on the creation of EmotiBlog, a fine-grained annotation scheme for labelling subjectivity in non-traditional textual genres. We also present the EmotiBlog corpus; a collection of blog posts composed by 270,000 tokens about 3 topics and in 3 languages: Spanish, English and Italian. Additionally, we carry out a series of experiments focused on checking the robustness of the model and its applicability to Natural Language Processing tasks with regards to the 3 languages. The experiments for the inter-annotator agreement, as well as for feature selection, provided satisfactory results, which have given an impetus to continue working with the model and extend the annotated corpus. In order to check its applicability, we tested different Machine Learning models created using the annotation in EmotiBlog on other corpora in order to see if the obtained annotation is domain and genre independent, obtaining positive results. Finally, we also applied EmotiBlog to Opinion Mining, proving that our resource allows an improvement the performance of systems built for this task.     

Genre as noise: noise in genre

Given a specific information need, documents of the wrong genre can be considered as noise. From this perspective, genre classification helps to separate relevant documents from noise. Orthographic errors represent a second, finer notion of noise. Since specific genres often include documents with many errors, an interesting question is whether this “micro-noise” can help to classify genre. In this paper we consider both problems. After introducing a comprehensive hierarchy of genres, we present an intuitive method to build specialized and distinctive classifiers that also work for very small training corpora. Special emphasis is given to the selection of intelligent high-level features. We then investigate the correlation between genre and micro noise. Using special error dictionaries, we estimate the typical error rates for each genre. Finally, we test if the error rate of a document represents a useful feature for genre classification.