Automatic query-based keyword and keyphrase extraction

Automatic key phrase extraction is fundamental to the success of many recent digital library applications and semantic information retrieval techniques and a difficult and essential problem in Vietnamese natural language processing (NLP). In this work, we propose a novel method for key phrase extracting of Vietnamese text that exploits the Vietnamese Wikipedia as an ontology and exploits specific characteristics of the Vietnamese language for the key phrase selection stage. We also explore NLP techniques that we propose for the analysis of Vietnamese texts, focusing on the advanced candidate phrases recognition phase as well as part-of-speech (POS) tagging. Finally, we review the results of several experiments that have examined the impacts of strategies chosen for Vietnamese key phrase extracting.

Keywords can be considered as condensed versions of documents, which can play important role in some text processing tasks such as text indexing, summarization and categorization. However, there are many digital documents especially on the Internet that do not have a list of assigned keywords. Assigning keywords to these documents manually is a difficult task and requires appropriate knowledge of the topic. Automatic keyword extraction process can solve this problem. In this paper, we introduce a new improved method for keyword extraction using random walk model by considering position of terms within the document and information gain of terms corresponds to the whole set of documents. We also incorporate mutual information (MI) of terms with random walk model to extract keywords from documents. The experiments on standard test collections show that our method outperforms the previously proposed methods.

Keyphrases are multipurpose knowledge gems, rendering keyphrase extraction a very important document processing task. They constitute a concise summary of documents that is extremely useful both for human inspection and machine consumption, in support of a number of tasks in the field of Natural Language Processing, Machine Learning and Information Retrieval. To this regard, the aim of this tutorial is two-fold: (a) to provide an overview of the automatic keyphrase extraction task and (b) to familiarize participants with the keyphrase extraction process. Specifically, we will provide a well‐structured review of the existing work, offer interesting insights on the different evaluation approaches, highlight open issues such as the need for evaluation approaches that take the semantic similarity of predicted and golden keyphrases, present a comparative experimental study of popular techniques, and familiarize the audience with the keyphrase extraction process via a demo presentation using jupyter notebook, which will be available to the audience during the practical part of the tutorial. We expect the tutorial to help newcomers and veterans alike navigate the large amount of prior art and grasp its evolution.

A Flexible Keyphrase Extraction Technique for Academic Literature

This research addresses the problem of automatic keyphrase extraction from large documents and back of the book indexing. The potential benefits of automating this process are far reaching, from improving information retrieval in digital libraries, to saving countless man-hours by helping professional indexers creating back of the book indexes. The dissertation introduces a new methodology to evaluate automated systems, which allows for a detailed, comparative analysis of several techniques for keyphrase extraction. We introduce and evaluate both supervised and unsupervised techniques, designed to balance the resource requirements of an automated system and the best achievable performance. Additionally, a number of novel features are proposed, including a statistical informativeness measure based on chi statistics; an encyclopedic feature that taps into the vast knowledge base of Wikipedia to establish the likelihood of a phrase referring to an informative concept; and a linguistic feature based on sophisticated semantic analysis of the text using current theories of discourse comprehension. The resulting keyphrase extraction system is shown to outperform the current state of the art in supervised keyphrase extraction by a large margin. Moreover, a fully automated back of the book indexing system based on the keyphrase extraction system was shown to lead to back of the book indexes closely resembling those created by human experts.

Due to the exponential overflow of textual information in various fields of knowledge and on the internet, it is very challenging to extract important information or to generate a summary from some multi-document collection in a specific field. With such a gigantic amount of textual content, human text summarization becomes impractical since it is expensive and consumes a lot of time and effort. So, developing automatic text summarization (ATS) systems is becoming increasingly essential. ATS approaches are either extractive or abstractive. The extractive approach is simpler and faster than the abstractive approach. This work proposes an extractive ATS system that aims to extract a small subset of sentences from a large multi-document text. First, the whole text is preprocessed by applying some natural language processing techniques such as sentences segmentation, words tokenization, removal of stop-words, and stemming to provide a structured representation of the original document collection. Based on this structured representation, the ATS problem is formulated as a multi-objective optimization (MOO) problem that optimizes the extracted summary to maintain the coverage of the main text content while avoiding redundant information. Secondly, an evolutionary sparse multi-objective algorithm is developed to solve the formulated large-scale MOO. The output of this algorithm is a set of non-dominated summaries (Pareto front). A novel criterion is proposed to select the target summary from the Pareto front. The proposed ATS system has been examined using (DUC) datasets, and the output summaries have been evaluated using (ROUGE) metrics and compared with the literature.

PurposeAutomatic keyphrase extraction (AKE) is an important task for grasping the main points of the text. In this paper, we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.Design/methodology/approachWe regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT, which was released by Google in 2018. We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain, which contains 100,000 abstracts as training set, 6,000 abstracts as development set and 3,094 abstracts as test set. We use unsupervised keyphrase extraction methods including term frequency (TF), TF-IDF, TextRank and supervised machine learning methods including Conditional Random Field (CRF), Bidirectional Long Short Term Memory Network (BiLSTM), and BiLSTM-CRF as baselines. Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.FindingsCompared with character-level BiLSTM-CRF, the best baseline model with F1 score of 50.16%, our character-level sequence labeling model based on BERT obtains F1 score of 59.80%, getting 9.64% absolute improvement.Research limitationsWe just consider automatic keyphrase extraction task rather than keyphrase generation task, so only keyphrases that are occurred in the given text can be extracted. In addition, our proposed dataset is not suitable for dealing with nested keyphrases.Practical implicationsWe make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts (CAKE) publicly available for the benefits of research community, which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.Originality/valueBy designing comparative experiments, our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models. And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

In Keyword Search Over Relational Databases (KSORD), retrieval of user's initial query is often unsatisfying. User has to reformulate his query and execute the new query, which costs much time and effort. In this paper, a method of automatically reformulating user queries by relevance feedback is introduced, which is named VSM-RF. Aimed at the results of KSORD systems, VSM-RF adopts a ranking method based on vector space model to rank KSORD results. After the first time of retrieval, using user feedback or pseudo feedback just as user like, VSM-RF computes expansion terms based on probability and reformulates the new query using query expansion. After KSORD systems executing the new query, more relevant results are produced by the new query in the result list and presented to user. Experimental results verify this method's effectiveness. ©2009 IEEE.

The keyphrase is a sentence or a part of a sentence that contains a sequence of words that expresses the meaning and the purpose of any given paragraph. Keyphrase extraction is the task of identifying the possible keyphrases from a given document. Many applications including text summarization, indexing, and characterization use keyphrase extraction. Also, it is an essential task to improve the performance of any information retrieval system. The internet contains a massive amount of documents that may have been manually assigned keyphrases or not. The Arabic language is an important language in the world. Nowadays the number of online Arabic documents is growing rapidly; and most of them have no manually assigned keyphrases, so the user will scan the whole retrieved web documents. To avoid scanning the entire retrieved document, we need keyphrases assigned to each web document manually or automatically. This paper addresses the problem of identifying keyphrases in Arabic documents automatically. In this work, we provide a novel algorithm that identified keyphrases from Arabic text. The new algorithm, Automatic Keyphrases Extraction from Arabic (AKEA), extracts keyphrases from Arabic documents automatically. In order to test the algorithm, we collected a dataset containing 100 documents from Arabic wiki; also, we downloaded another 56 agricultural documents from Food and Agricultural Organization of the United Nations (F.A.O.). The evaluation results show that the system achieves 83% precision value in identifying 2-word and 3-word keyphrases from agricultural domains.

Social network contains large amounts of user interests and preferences which may therefore help improve techniques for information retrieval, such as user profile construction and query expansion. By the manual annotation, we find that query intention normally consists of several retrieval behaviors and a certain number of social network behaviors. This paper argues that a user's query intention has a strong correlation with the corresponding social network contents, which focuses on two aspects. First, we analyze the proportion of social network websites in users' retrieval records, and evaluate the degree of relationship between information retrieval behavior and social network behavior from a statistic perspective. Second, a similarity between the two components of query intention, retrieval content and social network content, is calculated employing a topic model to measure the correlation of them from a semantic perspective. The evaluation on a ground-truth data of retrieval records indicates that a long-term retrieval procedure is usually accompanied with accessing social network websites. More importantly, the experimental results show that more than 54% of the content from social network has a strong correlation with users' query intention.

Background: Automatic keyphrase extraction (AKE) is essential to many NLP and information retrieval tasks. Extracting high-quality key phrases is difficult due to technological advancements and the exponential growth ‎of ‎textual data and digital sources. Unsupervised keyphrase extraction with cheap computing cost that relies ‎on ‎heuristic notions of phrase importance such as embedding similarities but their development necessitates in-depth subject expertise. Materials and Methods: This paper presents a method to obtain a semantic understanding of the query ‎and index documents by using ‎the embedding technique(universal Sentence ‎encoder (USE) ) while keeping the most informative ‎using Maximal Marginal Relevance (MMR) and then scoring(an ‎inverted index) the most documents relevant to the query ‎vector to improve the ‎performance of IR systems.‎ Results: The proposed retrieval model implement on the (Fire2011) dataset. The final ‎stage was evaluating the results of the baseline and the results (indexing and ranking) by using mean average precision (MAP). The ‎result of the baseline was 0.61, while the result ‎inverted index was 0.6277519 .‎ Conclusions: In this paper, we have discussed document retrieval using keep key phrases that ‎have ‏informativeness ‎properties by ‎using maximal ‎marginal ‎relevance, since if we extract a fixed number of top ‎keyphrases, ‎redundancy ‎hinders the diversification of the ‎extracted keyphrases.

As a typical unsupervised learning method, the TextRank algorithm performs well for large-scale text mining, especially for automatic summarization or keyword extraction. However, TextRank only considers the similarities between sentences in the processes of automatic summarization and neglects information about text structure and context. To overcome these shortcomings, the authors propose an improved highly-scalable method, called iTextRank. When building a TextRank graph in their new method, the authors compute sentence similarities and adjust the weights of nodes by considering statistical and linguistic features, such as similarities in titles, paragraph structures, special sentences, sentence positions and lengths. Their analysis shows that the time complexity of iTextRank is comparable with TextRank. More importantly, two experiments show that iTextRank has a higher accuracy and lower recall rate than TextRank, and it is as effective as several popular online automatic summarization systems.

An information retrieval system should provide references to the set of documents the user must evaluate in order to satisfy his/her information requirements. A major concern in this evaluation process is whether or not a document meets the user's needs. In many document retrieval systems there is no relevant information regarding the content of the documents. This makes it very difficult to evaluate if a document is relevant to the user's initial query. This suggests the need for a method to compare the documents on a word by word basis. Fully automated methods are too complex and difficult to generalize upon. A semiautomated user driven method for document mapping is described in this paper. It should decrease the amount of time required to validate the information content of a query.

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon test.