Topic Detection And Tracking Research Articles

Using Incremental PLSI for Threshold-Resilient Online Event Analysis

The goal of online event analysis is to detect events and track their associated documents in real time from a continuous stream of documents generated by multiple information sources. Unlike traditional text categorization methods, event analysis approaches consider the temporal relations among documents. However, such methods suffer from the threshold-dependency problem, so they only perform well for a narrow range of thresholds. In addition, if the contents of a document stream change, the optimal threshold (that is, the threshold that yields the best performance) often changes as well. In this paper, we propose a threshold-resilient online algorithm, called the incremental probabilistic latent semantic indexing (IPLSI) algorithm, which alleviates the threshold-dependency problem and simultaneously maintains the continuity of the latent semantics to better capture the story line development of events. The IPLSI algorithm is theoretically sound and empirically efficient and effective for event analysis. The results of the performance evaluation performed on the topic detection and tracking (TDT)-4 corpus show that the algorithm reduces the cost of event analysis by as much as 15 percent ~ 20 percent and increases the acceptable threshold range by 200 percent to 300 percent over the baseline.

Incorporating topic transition in topic detection and tracking algorithms

Topics often transit among documents in a document collection. To improve the accuracy of the topic detection and tracking (TDT) algorithms in discovering topics or classifying documents, it is necessary to make full use of this kind of topic transition information. However, TDT algorithms usually find topics based on topic models, such as LDA, pLSI, etc., which are a kind of mixture model and make the topic transition difficult to be denoted and implemented. A topic transition model representation based on hidden Markov model is present, and learning the topic transition from documents is discussed. Based on the model, two TDT algorithms incorporating topic transition, i.e. topic discovering and document classifying, are provided to show the application of the proposed model. Experiments on two real-world document collections are done with the two algorithms, and performance comparison with other similar algorithm shows that the accuracy can achieve 93% for topic discovering in Reuters-21578, and 97.3% in document classifying. Furthermore, topic transition discovered by the algorithm on a dataset which was collected from a BBS website is consistent with the manual analysis results.

Storyline-based summarization for news topic retrospection

Electronics newspapers gradually become main sources for news readers. When facing the numerous reports on a series of events in a topic, a summary of stories from news reports will benefit news readers in reviewing the news topic efficiently. Besides identifying events and presenting news titles and keywords the TDT (Topic Detection and Tracking) techniques are used to do, a summarized text to present event evolution is necessary for general news readers to review events under a news topic. This paper proposes a topic retrospection process and implements the SToRe ( Story-line based Topic Retrospection) system that identifies various events under a news topic, and composes a summary that news readers can get the sketch of event evolution in the topic. It consists of three main functions: event identification, main storyline construction and storyline-based summarization. The constructed main storyline can remove the irrelevant events and present a main theme. The storyline-based summarization extracts the representative sentences and takes the main theme as the template to compose the summary. The storyline summary not only provides readers enough information to understand the development of a news topic, but also serves as an index for readers to search corresponding news reports. Following a design science paradigm, a lab experiment is conducted to evaluate the SToRe system in the question-and-answer (Q&A) setting. The experimental results show that SToRe enables news readers to effectively and efficiently capture the evolution of a news topic.

Dependency structure language model for topic detection and tracking

In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches.

Topic tracking with time granularity reasoning

Temporal information is an important attribute of a topic, and a topic usually exists in a limited period. Therefore, many researchers have explored the utilization of temporal information in topic detection and tracking (TDT). They use either a story's publication time or temporal expressions in text to derive temporal relatedness between two stories or a story and a topic. However, past research neglects the fact that people tend to express a time with different granularities as time lapses. Based on a careful investigation of temporal information in news streams, we propose a new strategy with time granularity reasoning for utilizing temporal information in topic tracking. A set of topic times, which as a whole represent the temporal attribute of a topic, are distinguished from others in the given on-topic stories. The temporal relatedness between a story and a topic is then determined by the highest coreference level between each time in the story and each topic time where the coreference level between a test time and a topic time is inferred from the two times themselves, their granularities, and the time distance between the topic time and the publication time of the story where the test time appears. Furthermore, the similarity value between an incoming story and a topic, that is the likelihood that a story is on-topic, can be adjusted only when the new story is both temporally and semantically related to the target topic. Experiments on two different TDT corpora show that our proposed method could make good use of temporal information in news stories, and it consistently outperforms the baseline centroid algorithm and other algorithms which consider temporal relatedness.

Use of place information for improved event tracking

The main purpose of topic detection and tracking (TDT) is to detect, group, and organize newspaper articles reporting on the same event. Since an event is a reported occurrence at a specific time and place and the unavoidable consequences, TDT can benefit from an explicit use of time and place information. In this work, we focused on place information, using time information as in the previous research. News articles were analyzed for their characteristics of place information, and a new topic tracking method was proposed to incorporate the analysis results on place information. Experiments show that appropriate use of place information extracted automatically from news articles indeed helps event tracking that identify news articles reporting on the same events.

TDT (Topic Detection and Tracking)

TDT (Topic Detection and Tracking)

Usefulness of temporal information automatically extracted from news articles for topic tracking

Temporal information plays an important role in natural language processing (NLP) applications such as information extraction, discourse analysis, automatic summarization, and question-answering. In the topic detection and tracking (TDT) area, the temporal information often used is the publication date of a message, which is readily available but limited in its usefulness. We developed a relatively simple NLP method for extracting temporal information from Korean news articles, with the goal of improving performance of TDT tasks. To extract temporal information, we make use of finite state automata and a lexicon containing timerevealing vocabulary. Extracted information is converted into a canonicalized representation of a time point or a time duration. We first evaluated and investigated the extraction and canonicalization methods for their accuracy and the extent to which temporal information extracted as such can help TDT tasks. The experimental results show that time information extracted from the text does indeed help to significantly improve both precision and recall.

Simple Semantics in Topic Detection and Tracking.

Topic Detection and Tracking (TDT) is a research initiative that aims at techniques to organize news documents in terms of news events. We propose a method that incorporates simple semantics into TDT by splitting the term space into groups of terms that have the meaning of the same type. Such a group can be associated with an external ontology. This ontology is used to determine the similarity of two terms in the given group. We extract proper names, locations, temporal expressions and normal terms into distinct sub-vectors of the document representation. Measuring the similarity of two documents is conducted by comparing a pair of their corresponding sub-vectors at a time. We use a simple perceptron to optimize the relative emphasis of each semantic class in the tracking and detection decisions. The results suggest that the spatial and the temporal similarity measures need to be improved. Especially the vagueness of spatial and temporal terms needs to be addressed.

A month to topic detection and tracking in Hindi

We describe the one-month (June 2003) effort to create a topic detection and tracking (TDT) system to support news stories in Hindi. The University of Massachusetts submitted results for three different TDT tasks in the DARPA surprise language evaluation. The official task was topic tracking, but we also provided results for the new event detection and topic detection (clustering) tasks. Our approach to all three tasks was based on the vector-space model of information retrieval. We also describe the process we used to create the relevance judgments used to evaluate the system. Results suggest that topic tracking effectiveness is comparable to that of TDT tracking systems in other languages. Results for clustering and new event detection indicate that parameter settings for those tasks are sensitive to the language being used.

Challenges in information retrieval and language modeling

Information retrieval (IR) research has reached a point where it is appropriate to assess progress and to define a research agenda for the next five to ten years. This report summarizes a discussion of IR research challenges that took place at a recent workshop. The attendees of the workshop considered information retrieval research in a range of areas chosen to give broad coverage of topic areas that engage information retrieval researchers. Those areas are retrieval models, cross-lingual retrieval, Web search, user modeling, filtering, topic detection and tracking, classification, summarization, question answering, metasearch, distributed retrieval, multimedia retrieval, information extraction, as well as testbed requirements for future work. The potential use of language modeling techniques in these areas was also discussed. The workshop identified major challenges within each of those areas. The following are recurring themes that ran throughout: • User and context sensitive retrieval • Multi-lingual and multi-media issues • Better target tasks • Improved objective evaluations • Substantially more labeled data • Greater variety of data sources • Improved formal models Contextual retrieval and global information access were identified as particularly important long-term challenges.

Robust Techniques for Organizing and Retrieving Spoken Documents

Information retrieval tasks such as document retrieval and topic detection and tracking (TDT) show little degradation when applied to speech recognizer output. We claim that the robustness of the process is because of inherent redundancy in the problem: not only are words repeated, but semantically related words also provide support. We show how document and query expansion can enhance that redundancy and make document retrieval robust to speech recognition errors. We show that the same effect is true for TDT′s tracking task, but that recognizer errors are more of an issue for new event and story link detection.

Neural Networks and Machine Learning for Natural Language Processing - Introduction

The rapid increase of information available on the Internet, much of it textual, calls for computational methods that efficiently organise and classify natural language documents and allow for automatic language acquisition. The last years have seen an increase in conferences, workshops, research degree programs and commercial activities in this area. While the application of neural networks and machine learning techniques to natural language processing is not a recent development, the renewed interest is driven by the need to manage the information explosion of the Internet. A number of new fields, such as Web- and text mining, audio mining and topic detection and tracking (TDT) have emerged and long established areas such as information retrieval and extraction are more relevant than ever. This special issue includes articles which address a number of core questions, including the investigation of the underlying theoretical foundations of machine learning, the implementation and evaluation of methods for learning natural language with neural networks or other machine learning approaches, and applications of such learning systems in various domains.

An overview of Broadcast News corpora

The LDC began its first Broadcast News (BN) speech collection in the spring of 1996, facing a host of challenges including IPR negotiations with broadcasters, establishment of new transcription conventions and tools, and a compressed schedule for creation and release of speech, transcripts and in-domain language model data. The amount of acoustic training data available for participants in the DARPA Hub4 English benchmark tests doubled from 50 h in 1996 to 100 h in 1997, and doubled again to 200 h in 1998. An additional 40 h has been made available as of the summer of 1999. The 1997 benchmark test also saw the addition of BN speech and transcripts in Spanish and Mandarin Chinese, though in lesser quantity, with 30 h of training data in each language. Supplements to the existing pronunciation lexicons in each language were also produced. More recently, the coordinated research project on topic detection and tracking (TDT) has called for a large collection of BN speech data, totaling about 1100 h in English and 300 h in Mandarin over two phases (TDT2 and TDT3), although the level of detail and quality in the TDT transcriptions is not comparable to that of the Hub4 collections.