Semantic spaces

Any natural language can be considered as a tool for producing large databases (consisting of texts, written, or discursive). This tool for its description in turn requires other large databases (dictionaries, grammars etc.). Nowadays, the notion of database is associated with computer processing and computer memory. However, a natural language resides also in human brains and functions in human communication, from interpersonal to intergenerational one. We discuss in this survey/research paper mathematical, in particular geometric, constructions, which help to bridge these two worlds. In particular, in this paper we consider the Vector Space Model of semantics based on frequency matrices, as used in Natural Language Processing. We investigate underlying geometries, formulated in terms of Grassmannians, projective spaces, and flag varieties. We formulate the relation between vector space models and semantic spaces based on semic axes in terms of projectability of subvarieties in Grassmannians and projective spaces. We interpret Latent Semantics as a geometric flow on Grassmannians. We also discuss how to formulate G\"ardenfors' notion of "meeting of minds" in our geometric setting.Comment: 32 pages, TeX, 1 eps figur

Knowledge-rich Image Gist Understanding Beyond Literal Meaning

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process

Thematically Reinforced Explicit Semantic Analysis

We present an extended, thematically reinforced version of Gabrilovich and Markovitch's Explicit Semantic Analysis (ESA), where we obtain thematic information through the category structure of Wikipedia. For this we first define a notion of categorical tfidf which measures the relevance of terms in categories. Using this measure as a weight we calculate a maximal spanning tree of the Wikipedia corpus considered as a directed graph of pages and categories. This tree provides us with a unique path of "most related categories" between each page and the top of the hierarchy. We reinforce tfidf of words in a page by aggregating it with categorical tfidfs of the nodes of these paths, and define a thematically reinforced ESA semantic relatedness measure which is more robust than standard ESA and less sensitive to noise caused by out-of-context words. We apply our method to the French Wikipedia corpus, evaluate it through a text classification on a 37.5 MB corpus of 20 French newsgroups and obtain a precision increase of 9-10% compared with standard ESA.Comment: 13 pages, 2 figures, presented at CICLing 201

Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

On the Effect of Semantically Enriched Context Models on Software Modularization

Many of the existing approaches for program comprehension rely on the linguistic information found in source code, such as identifier names and comments. Semantic clustering is one such technique for modularization of the system that relies on the informal semantics of the program, encoded in the vocabulary used in the source code. Treating the source code as a collection of tokens loses the semantic information embedded within the identifiers. We try to overcome this problem by introducing context models for source code identifiers to obtain a semantic kernel, which can be used for both deriving the topics that run through the system as well as their clustering. In the first model, we abstract an identifier to its type representation and build on this notion of context to construct contextual vector representation of the source code. The second notion of context is defined based on the flow of data between identifiers to represent a module as a dependency graph where the nodes correspond to identifiers and the edges represent the data dependencies between pairs of identifiers. We have applied our approach to 10 medium-sized open source Java projects, and show that by introducing contexts for identifiers, the quality of the modularization of the software systems is improved. Both of the context models give results that are superior to the plain vector representation of documents. In some cases, the authoritativeness of decompositions is improved by 67%. Furthermore, a more detailed evaluation of our approach on JEdit, an open source editor, demonstrates that inferred topics through performing topic analysis on the contextual representations are more meaningful compared to the plain representation of the documents. The proposed approach in introducing a context model for source code identifiers paves the way for building tools that support developers in program comprehension tasks such as application and domain concept location, software modularization and topic analysis

Utilizing Knowledge Bases In Information Retrieval For Clinical Decision Support And Precision Medicine

Accurately answering queries that describe a clinical case and aim at finding articles in a collection of medical literature requires utilizing knowledge bases in capturing many explicit and latent aspects of such queries. Proper representation of these aspects needs knowledge-based query understanding methods that identify the most important query concepts as well as knowledge-based query reformulation methods that add new concepts to a query. In the tasks of Clinical Decision Support (CDS) and Precision Medicine (PM), the query and collection documents may have a complex structure with different components, such as disease and genetic variants that should be transformed to enable an effective information retrieval. In this work, we propose methods for representing domain-specific queries based on weighted concepts of different types whether exist in the query itself or extracted from the knowledge bases and top retrieved documents. Besides, we propose an optimization framework, which allows unifying query analysis and expansion by jointly determining the importance weights for the query and expansion concepts depending on their type and source. We also propose a probabilistic model to reformulate the query given genetic information in the query and collection documents. We observe significant improvement of retrieval accuracy will be obtained for our proposed methods over state-of-the-art baselines for the tasks of clinical decision support and precision medicine

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page

Inferring Strategies for Sentence Ordering in Multidocument News Summarization

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies