My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections

Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations

Adapted TextRank for Term Extraction: a generic method of improving automatic term extraction algorithms

Automatic Term Extraction is a fundamental Natural Language Processing task often used in many knowledge acquisition processes. It is a challenging NLP task due to its high domain dependence: no existing methods can consistently outperform others in all domains, and good ATE is very much an unsolved problem. We propose a generic method for improving the ranking of terms extracted by a potentially wide range of existing ATE methods. We re-design the well-known TextRank algorithm to work at corpus level, using easily obtainable domain resources in the form of seed words or phrases, to compute a score for a word from the target dataset. This is used to refine a candidate term’s score computed by an existing ATE method, potentially improving the ranking of real terms to be selected for tasks such as ontology engineering. Evaluation shows consistent improvement on 10 state of the art ATE methods by up to 25 percentage points in average precision measured at top-ranked K candidates

Kontzeptuen arteko erlazio erauzle automatiko baten inplementazioa.

Bi kontzeptu emanik, kontzeptu horien artean dagoen erlazioa erauzten duen sistema bat garatu nahi da. Horretarako, gaur egun existitzen diren sistemak aztertuko dira lehenik eta behin. Ondoren, ikasketa automatikoan oinarritutako sistema garatuko da. Bi ataza nagusi definitu dira horretarako. Alde batetik, ikasketarako beharrezko izango den datu multzoaren sorkuntza eta bestetik, erlazio erauzlearen inplementazioa. Sistema martxan egotean, sistemaren ebaluazio bat burutuko da eta behar izanez gero, sistema hobetu egingo da

SemRe-Rank: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised PageRank

Automatic Term Extraction (ATE) deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a ‘one-size-fit-all’ solution that may never exist, we propose to develop generic methods to ‘enhance’ existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness—an often overlooked venue—into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute ‘semantic importance’ scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K’s), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top K, or up to 17 points in F1

A novel topic model for automatic term extraction

Automatic term extraction (ATE) aims at extracting domain-specific terms from a corpus of a certain domain. Termhood is one essential measure for judging whether a phrase is a term. Previous researches on termhood mainly depend on the word frequency information. In this paper, we propose to compute termhood based on semantic representation of words. A novel topic model, namely i-SWB, is developed to map the domain corpus into a latent semantic space, which is composed of some general topics, a background topic and a documents-specific topic. Experiments on four domains demonstrate that our approach outperforms the state-of-the-art ATE approaches.Department of ComputingRefereed conference pape