Nina Tahmasebi

Extractive Summarization using Continuous Vector Space Models

Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring similarity. We evaluate different compositions for sentence representation on a standard dataset using the ROUGE evaluation measures. Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization.

Using word sense discrimination on historic document collections

Word sense discrimination is the first, important step towards automatic detection of language evolution within large, historic document collections. By comparing the found word senses over time, we can reveal and use important information that will improve understanding and accessibility of a digital archive. Algorithms for word sense discrimination have been developed while keeping todays language in mind and have thus been evaluated on well selected, modern datasets. The quality of the word senses found in the discrimination step has a large impact on the detection of language evolution. Therefore, as a first step, we verify that word sense discrimination can successfully be applied to digitized historic documents and that the results correctly correspond to word senses. Because accessibility of digitized historic collections is influenced also by the quality of the optical character recognition (OCR), as a second step we investigate the effects of OCR errors on word sense discrimination results. All evaluations in this paper are performed on The Times Archive, a collection of newspaper articles from 1785 - 1985.

Visions and open challenges for a knowledge-based culturomics

The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential of, statistical methods. In this paper, we present a vision for a knowledge-based culturomics that complements traditional culturomics. We discuss the possibilities and challenges of combining knowledge-based methods with statistical methods and address major challenges that arise due to the nature of the data; diversity of sources, changes in language over time as well as temporal dynamics of information in general. We address all layers needed for knowledge-based culturomics, from natural language processing and relations to summaries and opinions.

On the Uses of Word Sense Change for Research in the Digital Humanities

With advances in technology and culture, our language changes. We invent new words, add or change meanings of existing words and change names of existing things. Unfortunately, our language does not carry a memory; words, expressions and meanings used in the past are forgotten over time. When searching and interpreting content from archives, language changes pose a great challenge. In this paper, we present results of automatic word sense change detection and show the utility for archive users as well as digital humanities’ research. Our method is able to capture changes that relate to the usage and culture of a word that cannot easily be found using dictionaries or other resources.

Named entity evolution recognition on the Blogosphere

Advancements in technology and culture lead to changes in our language. These changes create a gap between the language known by users and the language stored in digital archives. It affects user’s possibility to firstly find content and secondly interpret that content. In a previous work, we introduced our approach for named entity evolution recognition (NEER) in newspaper collections. Lately, increasing efforts in Web preservation have led to increased availability of Web archives covering longer time spans. However, language on the Web is more dynamic than in traditional media and many of the basic assumptions from the newspaper domain do not hold for Web data. In this paper we discuss the limitations of existing methodology for NEER. We approach these by adapting an existing NEER method to work on noisy data like the Web and the Blogosphere in particular. We develop novel filters that reduce the noise and make use of Semantic Web resources to obtain more information about terms. Our evaluation shows the potentials of the proposed approach.

Ambient bloom: new business, content, design and models to increase the semantic ambient media experience

Semantic ambient media are the novel trend in the world of media reaching from the pioneering subareas such as ambient advertising to the new and emerging subareas such as ambient assisted living. They will likely shape the upcoming years in terms of modeling smart environments and also media consumption and interaction. This work analyzes semantic ambient media by considering business models, content and media, interaction design and consumer experience, and methods and techniques that are important to create this new form of media. Discussion is led using the state-of-the-art event of the semantic ambient media, which is the annual international workshop on Semantic Ambient Media Experience (SAME). The study also creates a vision for how ambient media will evolve and how they will look like in the future decade.

Towards mobile language evolution exploitation

Knowing about the evolution of a term can significantly help when searching for relevant information, especially in case of sudden evolutions (e.g. as of dramatical changes in political situations). Here, some terms get a completely new meaning or are used in new or different ways. In mobile situations it is important to be able to effectively retrieve information, since this is usually done in a hurry and interaction possibilities with mobile devices are limited. In this paper we describe a methodology using word sense discrimination to discover term evolution. We present two mobile interfaces for easy access and exploration of this evolution, as well as a user-study to show its usefulness. We conclude the paper with an outlook of further research possibilities in this new topic.

Every Word has its History: Interactive Exploration and Visualization of Word Sense Evolution

Human language constantly evolves due to the changing world and the need for easier forms of expression and communication. Our knowledge of language evolution is however still fragmentary despite significant interest of both researchers as well as wider public in the evolution of language. In this paper, we present an interactive framework that permits users study the evolution of words and concepts. The system we propose offers a rich online interface allowing arbitrary queries and complex analytics over large scale historical textual data, letting users investigate changes in meaning, context and word relationships across time.

Parameter Transfer across Domains for Word Sense Disambiguation.

Word sense disambiguation is defined as finding the corresponding sense for a target word in a given context, which comprises a major step in text applications. Recently, it has been addressed as an optimization problem. The idea behind is to find a sequence of senses that corresponds to the words in a given context with a maximum semantic similarity. Metaheuristics like simulated annealing and D-Bees provide approximate good-enough solutions, but are usually influenced by the starting parameters. In this paper, we study the parameter tuning for both algorithms within the word sense disambiguation problem. The experiments are conducted on different datasets to cover different disambiguation scenarios. We show that D-Bees is robust and less sensitive towards the initial parameters compared to simulated annealing, hence, it is sufficient to tune the parameters once and reuse them for different datasets, domains or languages.

Finding Individual Word Sense Changes and their Delay in Appearance.

We present a method for detecting word sense changes by utilizing automatically induced word senses. Our method works on the level of individual senses and allows a word to have e.g. one stable sense and then add a novel sense that later experiences change. Senses are grouped based on polysemy to find linguistic concepts and we can find broadening and narrowing as well as novel (polysemous and homonymic) senses. We evaluate on a testset, present recall and estimates of the time between expected and found change.