The tutorial is organized as follows: First, we discuss a little bit of background — what are keywords, and how does a keyword algorithm work?
Jee-Hyong Lee ejohn skku. Abstract Nowadays, automatic keyphrase extraction is considered to be an important task. Most of the previous studies focused only on selecting keyphrases within the body of input documents. These studies overlooked latent keyphrases that did not appear in documents.
In addition, a small number of studies on latent keyphrase extraction methods had some structural limitations. Although latent keyphrases do not appear in documents, they can still undertake an important role in text mining because they link meaningful concepts Keyphrase extraction contents of documents and can be utilized in short articles such as social network service, which rarely have explicit keyphrases.
In this paper, we propose a new approach that selects qualified latent keyphrases from input documents and overcomes some structural limitations by using deep belief networks in a supervised manner. The main idea of this Keyphrase extraction is to capture the intrinsic representations of documents and extract eligible latent keyphrases by using them.
Our experimental results showed that latent keyphrases were successfully extracted using our proposed method. Latent keyphrase, Deep belief networks, Weighted cost function, Keyphrase extraction 1. Introduction As the number of resources for documents is growing continuously, our need to acquire useful information from them is also growing everyday.
Keyphrase, which is the smallest unit of useful information, can concisely describe the meaning of content in documents. Moreover, keyphrases can also be used in text mining applications like information retrieval, summarization, document classification, and topic detection. However, only a small portion of documents contains author-assigned keyphrases and a majority of documents do not have keyphrases.
Therefore, extracting keyphrases from documents has become one of the main concerns in recent days, and there have been several studies on automatic keyphrase extraction task [ 1?
These studies overlooked latent keyphrases that did not appear in documents, extracted candidates only from the existing phrases in the document, and evaluated them under the assumption that they appear in the document.
Therefore, those methods were not suitable for the extraction of latent keyphrases. Although latent keyphrases do not appear in documents, they can still undertake an important role in text mining as they link meaningful concepts or contents of documents and can be utilized in short articles such as social network service SNSwhich rarely have explicit keyphrases In this paper, we propose a new approach that selects reliable latent keyphrases from input documents and overcomes some structural limitations by using deep belief networks DBNs in a supervised manner.
Additionally, a weighted cost function is suggested to handle the imbalanced environment of latent keyphrases compared to the candidates.
The remainder of this paper is organized as follows. Section 2 provides a brief description of previous methods in relation to keyphrase extraction. Section 3 provides a background on the proposed method. Section 4 introduces a method of latent keyphrase extraction.
Section 5 describes the experimental environment and evaluates the result. Section 6 provides a conclusion inferred from our work and indicates the direction of future research.
Related Work The algorithms for keyphrase extraction can be roughly categorized into two type: Initially, most of the previous extraction methods focused only on selecting the keyphrases within the body of input documents. Supervised algorithms proposed a binary approach, that is, determine whether a candidate is a keyphrase or not.
In general, supervised algorithms extracted multiple features from each candidate and applied machine learning techniques such as naive Bayes [ 1 ], support vector machine [ 2 ], and conditional random field [ 3 ].
The commonly used features were TF-IDF [ 4 ], the relative position of the first occurrence of a candidate in the document [ 1 ], and whether a candidate appeared in the title or subtitle [ 2 ].
However, these features were extracted under the assumption that the candidates appear in the document, so these algorithms are not suitable to evaluate and select latent keyphrases.
In the case of an unsupervised algorithm, a notable approach was to use a type of graph ranking model called, TextRank [ 5 ]. The major idea of this approach was that if a phrase had strong relationships with other phrases, it was an important phrase in the document.
This algorithm marked the phrases of the document as vertexes and assessed each vertex with their connected links, which was called a co-occurrence relationship.Step 2: Chunking and Extraction. For us to chunk the POS tagged text, we would have to first define what POS pattern we would consider as a chunk.
For e.g. an Adjective-Noun(s) combination (JJ-NN) can be a useful pattern to extract (in the example above this pattern would have . There is Rapid Automatic Keyword Extraction algorithm which defines two functions to decide if candidate words are keywords.
1) Remove all stop words from the text(eg for, the, are, is, and etc.) 2) create an array of candidate keywords which are set of words separated by stop words 3) find the. Keyphrase Extraction and Grouping Based on Association Rules Xin Li Advisor: University of Guelph, Professor Fei Song Keyphrases are important in capturing the content of a document and thus useful for text representation.
Keyphrase extraction is often needed for many natural language. Extractor is a patented key word and Keyphrase text analytics tools for software and app developers Evaluate Extractor - The Automatic Keyphrase Extraction Service A New World of Contextually Relevant Information.
Learning to Rank for Information Retrieval and Natural Language Processing: Second Edition (Synthesis Lectures on Human Language Technologies) [Hang Li] on timberdesignmag.com *FREE* shipping on qualifying offers. Learning to rank refers to machine learning techniques for training a model in a ranking task.
Learning to rank is useful for many applications in information retrieval. I am working on a project where I need to extract "technology related keywords/keyphrases" from text.
For example, my text is: "ABC Inc has been working on a project related to machine learning w.