•A classifier that is able to dynamically learn and recognize important word n-grams.•A novel text classifier having the ability to visually explain its rationale.•Support for incremental learning ...and text classification over text streams.•Efficient model for addressing early risk detection problems.
A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF’s eRisk open tasks. SS3 was designed to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important “on the fly”. This paper introduces τ-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that τ-SS3 is able to improve both current results and the richness of visual explanations.
False story detection is an important and challenging problem. This paper presents a simple and sound methodology that is able to automatically distinguish between true and false Hebrew stories using ...either psychological or semantic information. The examined corpus contains 96 stories that were composed by 48 native Hebrew speakers who were asked to tell both true and false stories. The features used by the classification model are word unigrams, bigrams, and trigrams. Different experiments on various combinations of these feature sets using five supervised machine learning (ML) methods, the InfoGain feature filtering method, and parameter tuning have been performed. We report on the success of this approach in identifying the correct types of stories. The word unigrams set was superior to all other feature sets. For the first classification task (true and false stories), the logistic regression ML method was the best method, achieving an accuracy of 91.67%. The two decision tree ML methods (J48 and REPTree) also present high accuracy results (90.63% and 87.5%) using only 5 and 4 unigrams, respectively.
We present a method for measuring similarity between source codes. We approach this task from the machine learning perspective using character and word
n
-grams as features and examining different ...machine learning algorithms. Furthermore, we explore the contribution of the latent semantic analysis in this task. We developed a corpus in order to evaluate the proposed approach. The corpus consists of around 10,000 source codes written in the Karel programming language to solve 100 different tasks. The results show that the highest classification accuracy is achieved when using Support Vector Machines classifier, applying the latent semantic analysis, and selecting as features trigrams of words.
Explorative data analysis in text mining essentially relies on effective visualization techniques which can expose hidden relationships among documents and reveal correspondence between documents and ...their features. In text mining, the documents are most often represented by feature vectors of very high dimensions, requiring dimensionality reduction to obtain visual projections in two- or three-dimensional space. Correspondence analysis is an unsupervised approach that allows for construction of low-dimensional projection space with simultaneous placement of both documents and features, making it ideal for explorative analysis in text mining. Its present use, however, has been limited to word-based features. In this paper, we investigate how this particular document representation compares to the representation with letter n-grams and word n-grams, and find that these alternative representations yield better results in separating documents of different class. We perform our experimental analysis on a bilingual Croatian-English parallel corpus, allowing us to additionally explore the impact of features in different languages on the quality of visualizations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
We present in this paper the results of a tagged corpus-based study conducted on two kinds of disfluencies (repeats and self-repairs) from a corpus of spontaneous spoken French. This work first ...investigates the linguistic features of both phenomena, and then shows how – from a corpus output tagged with TreeTagger – to take into account repeats and self-repairs using word N-grams model and rule-based pattern matching. Some results on a test corpus are finally presented.
The problem of Language Identification (LID) has been present in the Natural Language Processing domain for a relatively long period of time. There is a number of approaches based on statistical ...methods used for this particular task, and lately AI methods with the revival of neural network techniques. Some of the solutions described and implemented in the past show good performance, but texts that were processed were usually clean in terms of grammatical correctness and homogeneity. This paper explores and discusses LID in short and noisy messages written in similar languages, which is a non-trivial task, especially for very related languages. The experimentation methodology in the paper is based on the algorithms such as SVM, Naïve Bayes variants, Random Forest and Logistic Regression. In addition, we explore a novel distance based classification method - Common N-Grams (CNG). Finally, we explored whether Wikipedia as an additional training data source can improve a classifier performance.
In this paper the authors investigate the authorship of several short historical texts that are written by ten ancient Arabic travelers: this Arabic dataset, which was collected by the authors in ...2011, is called AAAT dataset. Several experiments of authorship attribution are conducted on these Arabic texts, by using different lexical features such as words, word-big rams, word-trig rams, word-tetra grams and rare words. Furthermore, seven different classifiers are employed, namely: Manhattan distance, Cosine distance, Stamatatos distance, Camberra distance, Multi Layer Perceptron (MLP), Sequential Minimal Optimization based Support Vector Machine (SMO-SVM) and Linear Regression. For the evaluation task, several experiments of authorship attribution are conducted on the AAAT dataset by using the different quoted features and classifiers. Results show good attribution performances with an optimal score of 80% of good authorship attribution. Moreover, this investigation has revealed interesting results concerning the Arabic language and more particularly for the short texts.
This paper proposes a method for exploring technical phrase frames by extracting word n-grams that match our information needs and interests from research paper titles. Technical phrase frames, the ...outcome of our method, are phrases with wildcards that may be substituted for any technical term. Our method, first of all, extracts word trigrams from research paper titles and constructs a co-occurrence graph of the trigrams. Even by simply applying Page Rank algorithm to the co-occurrence graph, we obtain the trigrams that can be regarded as technical key phrases at the higher ranks in terms of Page Rank score. In contrast, our method assigns weights to the edges of the co-occurrence graph based on Jaccard similarity between trigrams and then apply weighted Page Rank algorithm. Consequently, we obtain widely different but more interesting results. While the top-ranked trigrams obtained by unweighted Page Rank have just a self-contained meaning, those obtained by our method are technical phrase frames, i.e., A word sequence that forms a complete technical phrase only after putting a technical word (or words) before or/and after it. We claim that our method is a useful tool for discovering important phrase logical patterns, which can expand query keywords for improving information retrieval performance and can also work as candidate phrasings in technical writing to make our research papers attractive.
This is a comparative study to empirically investigate the performances of three different procedures for calculating authorship attribution likelihood ratios (LR). The procedures to be compared are: ...1) a procedure based on multivariate kernel density (MVKD) with lexical features; 2) a procedure based on word N-grams; and 3) a procedure based on character N-grams. Furthermore, the best-performing LRs of these three procedures are fused into combined single LRs using a logistic-regression fusion, in order to investigate the extent of the improvement/deterioration that the fusion brings about. This study uses chatlog messages, which were presented as evidence to prosecute paedophiles, for testing. The numbers of word tokens used to model the authorship attribution of each message group are 500 and 1000 words. This was done to examine the effect of sample size on the performance of a system. The performance of a system is assessed with regard to its validity (= accuracy) and reliability (= precision) using the log-likelihood-ratio cost (Cllr) and 95% credible intervals (CI), respectively. While describing the different characteristics of these three procedures in their outcomes, this study demonstrates that the MVKD procedure was the best-performing procedure out of the three in terms of Cllr . This study also demonstrates that a logistic-regression fusion is useful for combining the LRs obtained from the three procedures in question, resulting in a good improvement in performance.
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