Eduard Hoenkamp

Trading spaces: on the lore and limitations of latent semantic analysis

Two decades after its inception, Latent Semantic Analysis (LSA) has become part and parcel of every modern introduction to IR. For any tool that matures so quickly, it is important to check its lore and limitations, or else stagnation will set in. We focus here on the three main aspects of LSA that are well accepted, and the gist of which can be summarized as follows: (1) that LSA recovers latent semantic factors underlying the document space, (2) that such can be accomplished through lossy compression of the document space by eliminating lexical noise, and (3) that the latter can best be achieved by Singular Value Decomposition. For each aspect we performed experiments analogous to those reported in the LSA literature and compared the evidence brought to bear in each case. On the negative side, we show that the above claims about LSA are much more limited than commonly believed. Even a simple example may show that LSA does not recover the optimal semantic factors as intended in the pedagogical example used in many LSA publications. Additionally, and remarkably deviating from LSA lore, LSA does not scale up well: the larger the document space, the more unlikely that LSA recovers an optimal set of semantic factors. On the positive side, we describe new algorithms to replace LSA (and more recent alternatives as pLSA, LDA, and kernel methods) by trading its l2 space for an l1 space, thereby guaranteeing an optimal set of semantic factors. These algorithms seem to salvage the spirit of LSA as we think it was initially conceived.

How everyday language can and will boost effective information retrieval

Typing 2 or 3 keywords into a browser has become an easy and efficient way to find information. Yet, typing even short queries becomes tedious on ever shrinking (virtual) keyboards. Meanwhile, speech processing is maturing rapidly, facilitating everyday language input. Also, wearable technology can inform users proactively by listening in on their conversations or processing their social media interactions. Given these developments, everyday language may soon become the new input of choice.

Supporting content retrieval from WWW via “basic level categories” (poster abstract)

Much bandwidth on the Internet is spent on multimel Language: Many more attributes are listed for dia feeds for a rather limited range of entertainment. words at the basic level than for the superordiHowever, as IR matures, researchers are becoming more nate, and few additional for the subordinate [5]. aware of the professional possibilities of multimedia, For physical objects and organisms, parts notably and hence in effective techniques to find such inforproliferate at the basic level [6]. Also, when people mation [l]. On WWW, systems as ‘Visual Seek’ and have to name a picture of an object at the suborQBIC perform with limited success on closed databases dinate level, they choose the word for the basic in small domains. level.

Reinforcing trust in autonomous systems: A quantum cognitive approach

We investigated if an autonomous system can be provided with reasoning that maintains trust between human and system even when human and autonomous system reach discrepant conclusions. Tversky and Kahneman’s research [27] and the vast literature following it distinguishes two modes of human decision making: System 1, which is fast, emotional, and automatic, and System 2 which is slower, more deliberative, and more rational. Autonomous systems are thus far endowed with System 2. So when interacting with such a system, humans may follow System 1 unawares that their autonomous partner follows System 2. This can easily confuse the user when a discrepant decision is reached, eroding their trust in the autonomous system. Hence we investigated if trust in the message could interfere with trust its source, namely the autonomous system. For this we presented participants with images that might or might not be genuine, and found that they often distrusted the image (e.g., as photoshopped) when they distrusted its content. We present a quantum cognitive model that explains this interference. We speculate that enriching an autonomous system with this model will allow it to predict when its decisions may confuse the user, take pro-active steps to prevent this, and with it reinforce and maintain trust in the system.

About the 'Compromised Information Need' and Optimal Interaction as Quality Measure for Search Interfaces

Taylors concept of levels of information need has been cited in over a hundred IR publications since his work was first published. It concerns the phases a searcher goes through, starting with the feeling that information seems missing, to expressing a query to the system that hopefully will provide that information. As every year more IR publications reference Taylors work, but none of these so much as attempt to formalize the concept they use, it is doubtful that the term is always used with the same connotation. Hence we propose a formal definition of levels of information need, as especially in IR with its formal underpinnings, there is no excuse to leave frequently used terms undefined. We cast Taylors informally defined levels of information need --- and the transitions between them --- as an evolving dynamical system subsuming two subsystems: the searcher and the search engine. This moves the focus from optimizing the search engine to optimizing the search interface. We define the quality of an interface by how much users need to compromise in order to fill their information need. We show how a theoretical optimum can be calculated that assumes the least compromise from the user. This optimum can be used to establish a base-line for measuring how much a search interface deviates from the ideal, given actual search behavior, and by the same token offers a measure of comparison among competing interfaces.