Topical word importance for fast keyphrase extraction

We propose an improvement on a state-of-the-art keyphrase extraction algorithm, Topical PageRank (TPR), incorporating topical information from topic models. While the original algorithm requires a random walk for each topic in the topic model being used, ours is independent of the topic model, computing but a single PageRank for each text regardless of the amount of topics in the model. This increases the speed drastically and enables it for use on large collections of text using vast topic models, while not altering performance of the original algorithm

RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation

Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs derived from a given text can be used to efficiently identify and rank keywords. Introducing meta vertices (aggregates of existing vertices) and systematic redundancy filters, the proposed method performs on par with state-of-the-art for the keyword extraction task on 14 diverse datasets. The proposed method is unsupervised, interpretable and can also be used for document visualization.Comment: The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-31372-2_2

A tree based keyphrase extraction technique for academic literature

Automatic keyphrase extraction techniques aim to extract quality keyphrases to summarize a document at a higher level. Among the existing techniques some of them are domain-specific and require application domain knowledge, some of them are based on higher-order statistical methods and are computationally expensive, and some of them require large train data which are rare for many applications. Overcoming these issues, this thesis proposes a new unsupervised automatic keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, which is domain-independent, employs limited statistical knowledge, and requires no train data. The proposed technique also introduces a new variant of the binary tree, called KeyPhrase Extraction (KePhEx) tree to extract final keyphrases from candidate keyphrases. Depending on the candidate keyphrases the KePhEx tree structure is either expanded or shrunk or maintained. In addition, a measure, called Cohesiveness Index or CI, is derived that denotes the degree of cohesiveness of a given node with respect to the root which is used in extracting final keyphrases from a resultant tree in a flexible manner and is utilized in ranking keyphrases alongside Term Frequency. The effectiveness of the proposed technique is evaluated using an experimental evaluation on a benchmark corpus, called SemEval-2010 with total 244 train and test articles, and compared with other relevant unsupervised techniques by taking the representatives from both statistical (such as Term Frequency-Inverse Document Frequency and YAKE) and graph-based techniques (PositionRank, CollabRank (SingleRank), TopicRank, and MultipartiteRank) into account. Three evaluation metrics, namely precision, recall and F1 score are taken into consideration during the experiments. The obtained results demonstrate the improved performance of the proposed technique over other similar techniques in terms of precision, recall, and F1 scores

Creation and evaluation of large keyphrase extraction collections with multiple opinions

While several automatic keyphrase extraction (AKE) techniques have been developed and analyzed, there is little consensus on the definition of the task and a lack of overview of the effectiveness of different techniques. Proper evaluation of keyphrase extraction requires large test collections with multiple opinions, currently not available for research. In this paper, we (i) present a set of test collections derived from various sources with multiple annotations (which we also refer to as opinions in the remained of the paper) for each document, (ii) systematically evaluate keyphrase extraction using several supervised and unsupervised AKE techniques, (iii) and experimentally analyze the effects of disagreement on AKE evaluation. Our newly created set of test collections spans different types of topical content from general news and magazines, and is annotated with multiple annotations per article by a large annotator panel. Our annotator study shows that for a given document there seems to be a large disagreement on the preferred keyphrases, suggesting the need for multiple opinions per document. A first systematic evaluation of ranking and classification of keyphrases using both unsupervised and supervised AKE techniques on the test collections shows a superior effectiveness of supervised models, even for a low annotation effort and with basic positional and frequency features, and highlights the importance of a suitable keyphrase candidate generation approach. We also study the influence of multiple opinions, training data and document length on evaluation of keyphrase extraction. Our new test collection for keyphrase extraction is one of the largest of its kind and will be made available to stimulate future work to improve reliable evaluation of new keyphrase extractors

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11

Algoritmos de aprendizaje automático no supervisado para la extracción de palabras clave en trabajos de investigación de pregrado

La información que administra la Universidad Nacional del Altiplano de Puno, en los últimos años se ha visto incrementada sobre todo trabajos de investigación realizados por estudiantes y egresados de pregrado, para los que se usan técnicas empíricas para la selección de palabras clave, existiendo a la fecha métodos técnicos que ayuden en este proceso, en tanto el uso de tecnologías de información y comunicación han tomado relevancia e importancia en la administración y seguimiento de trabajos de investigación como la Plataforma de Investigación Integrada a la Labor Académica con Responsabilidad (PILAR), donde registra información de los proyectos de investigación como (Título, Resumen, Palabras Clave), en sus diferentes modalidades. En el presente trabajo de investigación se ha analizado 7430 registros de proyectos de investigación, a los cuales se realizaron predicciones con cada uno de los 09 modelos de aprendizaje automático no supervisado implementados. Los resultados nos muestran que el modelo TF-IDF, es el más eficiente en tiempo y en precisión de extracción de palabras clave, obteniendo un 72 % de precisión y en un tiempo de extracción entre [0.4786 ,SD 0.0501], por cada documento procesado por este modelo.Tesi

Pelabelan Klaster Artikel Ilmiah Menggunakan Topic Rank dan Maximum Common Subgraph

Metode klasterisasi dapat memudahkan pengelompokkan artikel ilmiah. Pelabelan klaster diperlukan untuk mengetahui frasa kunci yang merepresentasikan topik bahasan kelompok artikel ilmiah. Beberapa klaster artikel ilmiah perlu digabung karena masih memiliki kemiripan topik untuk memberikan hasil label klaster yang lebih baik. Kemiripan topik dapat diwakili dengan kesamaan relasi kata yang dimodelkan dengan graf. Penelitian ini memiliki usulan metode pelabelan klaster artikel ilmiah dengan proses penggabungan klaster berdasarkan kesamaan struktur graf representasi klaster. Usulan metode terdiri dari : (1) Pengelompokkan artikel ilmiah menggunakan metode klasterisasi K-Means++. (2) Ekstraksi kandidat frasa menggunakan Frequent Phrase Mining (FPM). (3) Konstruksi graf menggunakan kata – kata pembentuk frasa sebagai vertex dan relasi kata sebagai edge berdasarkan Word2Vec. (4) Penggabungan klaster dengan pengukuran similaritas klaster berdasarkan struktur Maximum Common Subgraph (MCS). (5) Pelabelan klaster pada hasil penggabungan klaster menggunakan metode TopicRank. Usulan metode dievaluasi pada 2 dataset artikel ilmiah yang memiliki variasi tingkat pemisahan dan kohesi klaster. Koherensi topik digunakan sebagai pengukuran evaluasi untuk mengukur tingkat keterkaitan topik label klaster pada sebuah klaster. Hasil pengujian menunjukkan bahwa dataset yang memiliki tingkat pemisahan dan kohesi klaster yang tinggi (homogen) menghasilkan koherensi topik label klaster gabungan yang lebih tinggi. Penggunaan relasi kata co-occurrence pada pembuatan graf representasi klaster menghasilkan koherensi topik yang lebih baik dibandingkan relasi kata Word2Vec. Hal ini disebabkan oleh relasi kata co-occurrence berbasis frekuensi sehingga merepresentasikan topik mayoritas klaster. ========================================================================================================== Unstructured scientific articles can benefited by clustering method to group scientific articles based on topic similarity. Cluster labeling on the yielded cluster is required to discover key phrases that best represent the topics covered. Several clusters still need to be bundled because they still have similar topics to give better cluster labels results. In addition to word occurences, the similarity of the topic can also be represented by word semantic relation that can be modeled with the graph. This research proposes labeling clusters of scientific articles with cluster merging as research contribution to provide a more representative label of cluster topics. This research proposed cluster labeling method with cluster merging process using graph model. Graph model approach is choosen because it can map the relationship between words, hence representing text semantic information. There are several stages in the proposed method. First, K-Means++ clustering method is applied on a collection of scientific articles. Second, for each cluster, phrase extraction is executed using Frequent Phrase Mining to get word tokens that capable to constitute representative phrase for cluster topics. Acquired word tokens used as input to constructing graph representation of a cluster. After that, cluster merging is done based on cluster graph similarity using Maximum Common Subgraph (MCS) method. Then, the cluster labeling process is performed on clusters that have been merged using the TopicRank method. Proposed method evaluated on 2 dataset based on the merged cluster label topic coherence score, using Word2Vec-based graph model and co-occurence-based graph model. Result show that homogenous dataset 1 yield better result than heterogenous dataset 2. In addition, the use of co-occurence-based graph produce prefereable result on cluster merging process

Using Machine Learning and Graph Mining Approaches to Improve Software Requirements Quality: An Empirical Investigation

Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts, thereby mitigating the fault propagation to later phases where the same faults are harder to find and fix. The output of an inspection process is list of faults that are present in software requirements specification document (SRS). The artifact author must manually read through the reviews and differentiate between true-faults and false-positives before fixing the faults. The first goal of this research is to automate the detection of useful vs. non-useful reviews. Next, post-inspection, requirements author has to manually extract key problematic topics from useful reviews that can be mapped to individual requirements in an SRS to identify fault-prone requirements. The second goal of this research is to automate this mapping by employing Key phrase extraction (KPE) algorithms and semantic analysis (SA) approaches to identify fault-prone requirements. During fault-fixations, the author has to manually verify the requirements that could have been impacted by a fix. The third goal of my research is to assist the authors post-inspection to handle change impact analysis (CIA) during fault fixation using NL processing with semantic analysis and mining solutions from graph theory. The selection of quality inspectors during inspections is pertinent to be able to carry out post-inspection tasks accurately. The fourth goal of this research is to identify skilled inspectors using various classification and feature selection approaches. The dissertation has led to the development of automated solution that can identify useful reviews, help identify skilled inspectors, extract most prominent topics/keyphrases from fault logs; and help RE author during the fault-fixation post inspection