StarSpace: Embed All The Things!

We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings. In each case the model works by embedding those entities comprised of discrete features and comparing them against each other -- learning similarities dependent on the task. Empirical results on a number of tasks show that StarSpace is highly competitive with existing methods, whilst also being generally applicable to new cases where those methods are not

Finding Structured and Unstructured Features to Improve the Search Result of Complex Question

The current researches on question answer usually achieve the answer only from unstructured text resources such as collection of news or pages. According to our observation from Yahoo!Answer, users sometimes ask in complex natural language questions which contain structured and unstructured features. Generally, answering the complex questions needs to consider not only unstructured but also structured resource. In this work, researcher propose a new idea to improve accuracy of the answers of complex questions by recognizing the structured and unstructured features of questions and them in the web. Our framework consists of three parts: Question Analysis, Resource Discovery, and Analysis of The Relevant Answer. In Question Analysis researcher used a few assumptions and tried to find structured and unstructured features of the questions. In the resource discovery researcher integrated structured data (relational database) and unstructured data (web page) to take the advantage of two kinds of data to improve and to get the correct answers. We can find the best top fragments from context of the relevant web pages in the Relevant Answer part and then researcher made a score matching between the result from structured data and unstructured data, then finally researcher used QA template to reformulate the questions. Penelitian yang ada pada saat ini mengenai Question Answer (QA) biasanya mendapatkan jawaban dari sumber teks yang tidak terstruktur seperti kumpulan berita atau halaman. Sesuai dengan observasi peneliti dari pengguna Yahoo!Answer, biasanya mereka bertanya dalam natural language yang sangat kompleks di mana mengandung bentuk yang terstruktur dan tidak terstruktur. Secara umum, menjawab pertanyaan yang kompleks membutuhkan pertimbangan yang tidak hanya sumber tidak terstruktur tetapi juga sumber yang terstruktur. Pada penelitian ini, peneliti mengajukan suatu ide baru untuk meningkatkan keakuratan dari jawaban pertanyaan yang kompleks dengan mengenali bentuk terstruktur dan tidak terstruktur dan mengintegrasikan keduanya di web. Framework yang digunakan terdiri dari tiga bagian: Question Analysis, Resource Discovery, dan Analysis of The Relevant Answer. Pada Question Analysis peneliti menggunakan beberapa asumsi dan mencoba mencari bentuk data yang terstruktur dan tidak terstruktur. Dalam penemuan sumber daya, peneliti mengintegrasikan data terstruktur (relational database) dan data tidak terstruktur (halaman web) untuk mengambil keuntungan dari dua jenis data untuk meningkatkan dan untuk mencapai jawaban yang benar. Peneliti dapat menemukan fragmen atas terbaik dari konteks halaman web pada bagian Relevant Answer dan kemudian peneliti membuat pencocoka skor antara hasil dari data terstruktur dan data tidak terstruktur. Terakhir peneliti menggunakan template QA untuk merumuskan pertanyaan

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents--or short passages--in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms--such as a person's name or a product model number--not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020

Neural Representations of Concepts and Texts for Biomedical Information Retrieval

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user\u27s query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the input query. Furthermore, current systems may also maintain preprocessed structured knowledge derived from textual data as so called knowledge graphs, so certain types of queries that are posed as questions can be parsed as such; a response can be an output of one or more named entities instead of a ranked list of documents (e.g., what diseases are associated with EGFR mutations? ). This refined setup is often termed as question answering (QA) in the IR and natural language processing (NLP) communities. In biomedicine and healthcare, specialized corpora are often at play including research articles by scientists, clinical notes generated by healthcare professionals, consumer forums for specific conditions (e.g., cancer survivors network), and clinical trial protocols (e.g., www.clinicaltrials.gov). Biomedical IR is specialized given the types of queries and the variations in the texts are different from that of general Web documents. For example, scientific articles are more formal with longer sentences but clinical notes tend to have less grammatical conformity and are rife with abbreviations. There is also a mismatch between the vocabulary of consumers and the lingo of domain experts and professionals. Queries are also different and can range from simple phrases (e.g., COVID-19 symptoms ) to more complex implicitly fielded queries (e.g., chemotherapy regimens for stage IV lung cancer patients with ALK mutations ). Hence, developing methods for different configurations (corpus, query type, user type) needs more deliberate attention in biomedical IR. Representations of documents and queries are at the core of IR methods and retrieval methodology involves coming up with these representations and matching queries with documents based on them. Traditional IR systems follow the approach of keyword based indexing of documents (the so called inverted index) and matching query phrases against the document index. It is not difficult to see that this keyword based matching ignores the semantics of texts (synonymy at the lexeme level and entailment at phrase/clause/sentence levels) and this has lead to dimensionality reduction methods such as latent semantic indexing that generally have scale-related concerns; such methods also do not address similarity at the sentence level. Since the resurgence of neural network methods in NLP, the IR field has also moved to incorporate advances in neural networks into current IR methods. This dissertation presents four specific methodological efforts toward improving biomedical IR. Neural methods always begin with dense embeddings for words and concepts to overcome the limitations of one-hot encoding in traditional NLP/IR. In the first effort, we present a new neural pre-training approach to jointly learn word and concept embeddings for downstream use in applications. In the second study, we present a joint neural model for two essential subtasks of information extraction (IE): named entity recognition (NER) and entity normalization (EN). Our method detects biomedical concept phrases in texts and links them to the corresponding semantic types and entity codes. These first two studies provide essential tools to model textual representations as compositions of both surface forms (lexical units) and high level concepts with potential downstream use in QA. In the third effort, we present a document reranking model that can help surface documents that are likely to contain answers (e.g, factoids, lists) to a question in a QA task. The model is essentially a sentence matching neural network that learns the relevance of a candidate answer sentence to the given question parametrized with a bilinear map. In the fourth effort, we present another document reranking approach that is tailored for precision medicine use-cases. It combines neural query-document matching and faceted text summarization. The main distinction of this effort from previous efforts is to pivot from a query manipulation setup to transforming candidate documents into pseudo-queries via neural text summarization. Overall, our contributions constitute nontrivial advances in biomedical IR using neural representations of concepts and texts

Entity search: How to build virtual documents leveraging on graph embeddings

We develop an innovative method for the Information Retrieval Entity Search task. We propose a new approach that exploits graph embedding techniques and clustering in order to create the documents necessary for the retrieval, in particular we create a document for a set of related entities. The main advantage of our implementation is that our systems could return to the user not only entities that directly match the user query, but also relevant entities that are not explicitly mentioned

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

Approaches to implement and evaluate aggregated search

La recherche d'information agrégée peut être vue comme un troisième paradigme de recherche d'information après la recherche d'information ordonnée (ranked retrieval) et la recherche d'information booléenne (boolean retrieval). Les deux paradigmes les plus explorés jusqu'à aujourd'hui retournent un ensemble ou une liste ordonnée de résultats. C'est à l'usager de parcourir ces ensembles/listes et d'en extraire l'information nécessaire qui peut se retrouver dans plusieurs documents. De manière alternative, la recherche d'information agrégée ne s'intéresse pas seulement à l'identification des granules (nuggets) d'information pertinents, mais aussi à l'assemblage d'une réponse agrégée contenant plusieurs éléments. Dans nos travaux, nous analysons les travaux liés à la recherche d'information agrégée selon un schéma général qui comprend 3 parties: dispatching de la requête, recherche de granules d'information et agrégation du résultat. Les approches existantes sont groupées autours de plusieurs perspectives générales telle que la recherche relationnelle, la recherche fédérée, la génération automatique de texte, etc. Ensuite, nous nous sommes focalisés sur deux pistes de recherche selon nous les plus prometteuses: (i) la recherche agrégée relationnelle et (ii) la recherche agrégée inter-verticale. * La recherche agrégée relationnelle s'intéresse aux relations entre les granules d'information pertinents qui servent à assembler la réponse agrégée. En particulier, nous nous sommes intéressés à trois types de requêtes notamment: requête attribut (ex. président de la France, PIB de l'Italie, maire de Glasgow, ...), requête instance (ex. France, Italie, Glasgow, Nokia e72, ...) et requête classe (pays, ville française, portable Nokia, ...). Pour ces requêtes qu'on appelle requêtes relationnelles nous avons proposés trois approches pour permettre la recherche de relations et l'assemblage des résultats. Nous avons d'abord mis l'accent sur la recherche d'attributs qui peut aider à répondre aux trois types de requêtes. Nous proposons une approche à large échelle capable de répondre à des nombreuses requêtes indépendamment de la classe d'appartenance. Cette approche permet l'extraction des attributs à partir des tables HTML en tenant compte de la qualité des tables et de la pertinence des attributs. Les différentes évaluations de performances effectuées prouvent son efficacité qui dépasse les méthodes de l'état de l'art. Deuxièmement, nous avons traité l'agrégation des résultats composés d'instances et d'attributs. Ce problème est intéressant pour répondre à des requêtes de type classe avec une table contenant des instances (lignes) et des attributs (colonnes). Pour garantir la qualité du résultat, nous proposons des pondérations sur les instances et les attributs promouvant ainsi les plus représentatifs. Le troisième problème traité concerne les instances de la même classe (ex. France, Italie, Allemagne, ...). Nous proposons une approche capable d'identifier massivement ces instances en exploitant les listes HTML. Toutes les approches proposées fonctionnent à l'échelle Web et sont importantes et complémentaires pour la recherche agrégée relationnelle. Enfin, nous proposons 4 prototypes d'application de recherche agrégée relationnelle. Ces derniers peuvent répondre des types de requêtes différents avec des résultats relationnels. Plus précisément, ils recherchent et assemblent des attributs, des instances, mais aussi des passages et des images dans des résultats agrégés. Un exemple est la requête ``Nokia e72" dont la réponse sera composée d'attributs (ex. prix, poids, autonomie batterie, ...), de passages (ex. description, reviews, ...) et d'images. Les résultats sont encourageants et illustrent l'utilité de la recherche agrégée relationnelle. * La recherche agrégée inter-verticale s'appuie sur plusieurs moteurs de recherche dits verticaux tel que la recherche d'image, recherche vidéo, recherche Web traditionnelle, etc. Son but principal est d'assembler des résultats provenant de toutes ces sources dans une même interface pour répondre aux besoins des utilisateurs. Les moteurs de recherche majeurs et la communauté scientifique nous offrent déjà une série d'approches. Notre contribution consiste en une étude sur l'évaluation et les avantages de ce paradigme. Plus précisément, nous comparons 4 types d'études qui simulent des situations de recherche sur un total de 100 requêtes et 9 sources différentes. Avec cette étude, nous avons identifiés clairement des avantages de la recherche agrégée inter-verticale et nous avons pu déduire de nombreux enjeux sur son évaluation. En particulier, l'évaluation traditionnelle utilisée en RI, certes la moins rapide, reste la plus réaliste. Pour conclure, nous avons proposé des différents approches et études sur deux pistes prometteuses de recherche dans le cadre de la recherche d'information agrégée. D'une côté, nous avons traité trois problèmes importants de la recherche agrégée relationnelle qui ont porté à la construction de 4 prototypes d'application avec des résultats encourageants. De l'autre côté, nous avons mis en place 4 études sur l'intérêt et l'évaluation de la recherche agrégée inter-verticale qui ont permis d'identifier les enjeux d'évaluation et les avantages du paradigme. Comme suite à long terme de ce travail, nous pouvons envisager une recherche d'information qui intègre plus de granules relationnels et plus de multimédia.Aggregated search or aggregated retrieval can be seen as a third paradigm for information retrieval following the Boolean retrieval paradigm and the ranked retrieval paradigm. In the first two, we are returned respectively sets and ranked lists of search results. It is up to the time-poor user to scroll this set/list, scan within different documents and assemble his/her information need. Alternatively, aggregated search not only aims the identification of relevant information nuggets, but also the assembly of these nuggets into a coherent answer. In this work, we present at first an analysis of related work to aggregated search which is analyzed with a general framework composed of three steps: query dispatching, nugget retrieval and result aggregation. Existing work is listed aside different related domains such as relational search, federated search, question answering, natural language generation, etc. Within the possible research directions, we have then focused on two directions we believe promise the most namely: relational aggregated search and cross-vertical aggregated search. * Relational aggregated search targets relevant information, but also relations between relevant information nuggets which are to be used to assemble reasonably the final answer. In particular, there are three types of queries which would easily benefit from this paradigm: attribute queries (e.g. president of France, GDP of Italy, major of Glasgow, ...), instance queries (e.g. France, Italy, Glasgow, Nokia e72, ...) and class queries (countries, French cities, Nokia mobile phones, ...). We call these queries as relational queries and we tackle with three important problems concerning the information retrieval and aggregation for these types of queries. First, we propose an attribute retrieval approach after arguing that attribute retrieval is one of the crucial problems to be solved. Our approach relies on the HTML tables in the Web. It is capable to identify useful and relevant tables which are used to extract relevant attributes for whatever queries. The different experimental results show that our approach is effective, it can answer many queries with high coverage and it outperforms state of the art techniques. Second, we deal with result aggregation where we are given relevant instances and attributes for a given query. The problem is particularly interesting for class queries where the final answer will be a table with many instances and attributes. To guarantee the quality of the aggregated result, we propose the use of different weights on instances and attributes to promote the most representative and important ones. The third problem we deal with concerns instances of the same class (e.g. France, Germany, Italy ... are all instances of the same class). Here, we propose an approach that can massively extract instances of the same class from HTML lists in the Web. All proposed approaches are applicable at Web-scale and they can play an important role for relational aggregated search. Finally, we propose 4 different prototype applications for relational aggregated search. They can answer different types of queries with relevant and relational information. Precisely, we not only retrieve attributes and their values, but also passages and images which are assembled into a final focused answer. An example is the query ``Nokia e72" which will be answered with attributes (e.g. price, weight, battery life ...), passages (e.g. description, reviews ...) and images. Results are encouraging and they illustrate the utility of relational aggregated search. * The second research direction that we pursued concerns cross-vertical aggregated search, which consists of assembling results from different vertical search engines (e.g. image search, video search, traditional Web search, ...) into one single interface. Here, different approaches exist in both research and industry. Our contribution concerns mostly evaluation and the interest (advantages) of this paradigm. We propose 4 different studies which simulate different search situations. Each study is tested with 100 different queries and 9 vertical sources. Here, we could clearly identify new advantages of this paradigm and we could identify different issues with evaluation setups. In particular, we observe that traditional information retrieval evaluation is not the fastest but it remains the most realistic. To conclude, we propose different studies with respect to two promising research directions. On one hand, we deal with three important problems of relational aggregated search following with real prototype applications with encouraging results. On the other hand, we have investigated on the interest and evaluation of cross-vertical aggregated search. Here, we could clearly identify some of the advantages and evaluation issues. In a long term perspective, we foresee a possible combination of these two kinds of approaches to provide relational and cross-vertical information retrieval incorporating more focus, structure and multimedia in search results